14 Best Artificial Intelligence Programming Language in 2025

Artificial Intelligence (AI) is now a widely discussed and researched field, with major tech companies and startups actively involved in its development.

The scope of AI ranges from basic calculators to self-steering technology and even self-aware robots, hinting at the potential to reshape the future. The primary goal of AI is to create systems that match or surpass human intelligence.

Mark Cuban, an American businessman and investor, predicts that the world’s first trillionaire will emerge from mastering AI and its various applications.

The first trillionaire is going to be someone who masters Artificial Intelligence and all its derivatives and implements it in ways we never thought of —  Mark Cuban.

If you’re an aspiring AI enthusiast unsure about which coding language to choose for your next project, you’ve come to the right place. Below, we’ll highlight the best programming languages that will allow you to unlock the full potential of artificial intelligence.

Did you know? 

While MATLAB first appeared in the late 1970s, it remains a key player in academic and research settings, particularly in fields that heavily involve mathematical simulation and testing AI algorithms. It has more than 4 million users globally.

11. R

R is specifically designed for statistical computing and data analysis. It has gained popularity for its strengths in statistical modeling, exploratory data analysis, and data visualization. 

With a vast ecosystem of libraries and packages, R is well-suited for various AI tasks. For instance, the “caret” (Classification And REgression Training) package offers a unified interface for diverse machine learning algorithms, simplifying the comparison and evaluation of different models.

Other packages like “tm” and “quanteda” find applications in text mining and natural language processing. These packages can process and analyze textual data efficiently, which makes them valuable for sentiment analysis, document clustering, and topic modeling.

Data scientists and researchers often prefer R for complex tasks, particularly those involving regression analysis, hypothesis testing, and probability distributions.

10. MATLAB

MATLAB (MATrix LABoratory) excels in data analysis, numerical computing, and visualization. Its extensive set of built-in functions and toolboxes make it a popular choice for prototyping and implementing complex algorithms in several domains, including AI. 

MATLAB has special toolboxes for deep learning, machine learning, and artificial intelligence. These toolboxes offer pre-built algorithms and models to simplify the development of AI apps. Popular examples include the Image Processing Toolbox, the Deep Learning Toolbox, and the Statistics and Machine Learning Toolbox.

The platform is widely used in signal and image processing applications, making it invaluable for tasks like vision and speech recognition within the AI domain.

It also provides robust charting and plotting tools for effective visualization. These tools allow you to create detailed graphs, images, and charts, facilitating the interpretation of AI models and data.

9. C#

One can use C# on Microsoft’s .NET suite to develop high-level machine learning models. It’s an object-oriented language that contains several useful tools for developing AI apps.

The ML.NET, a cross-platform machine learning framework, makes it easy to develop and integrate custom machine learning models into .NET apps. You don’t even require any prior machine learning experience to do it.

You can even use pre-built AI models, such as vision and speech recognition, intelligent search, and emotion and sentiment detection.

.NET also supports the F# programming language — a succinct, robust, and performant language. Its type system and special features like Units of Measure and Type Providers are a perfect match for machine learning and data science.

8. Julia

Benchmark times relative to C (C performance = 1.0; smaller is better)

Julia is designed to deal with high-performance numerical analysis and computational science without the typical requirement of separate compilation. It includes a type system with parametric polymorphism and multiple dispatches as its core programming paradigm.

Julia’s deep mathematical roots and comprehensive customizability make it very friendly to work with for a data analyst. With Julia, it’s quite easy to translate algorithms from research papers into code with no loss in translation – thus decreasing model risk and improving safety.

However, the most important factor is the community – It’s an open-source language, licensed under the liberal MIT license. Julia runs on almost all types of hardware out there, including IBM, Intel, NVIDIA, and ARM.

Since Julia combines the ease of use and familiar syntax of Matlab, Python, and R with the speed of C++, developers no longer need to estimate models in one language and reproduce them in a faster production language. This reduces error and saves time and cost.

This language is now used by the world’s biggest companies. IBM and Julia computing, for instance, have analyzed eye fundus images and developed deep-learning solutions that provide better eye diagnosis and care to thousands of rural Indians. It is also widely used in astronomy, robotics, network security, parallel supercomputing, and financial modeling and management.

7. Haskell

Haskell in strong static typing, a non-strict programming language developed in 1990. Since there are not many Haskell developers, private companies are reluctant to try Haskell.

One thing that Haskell is perfect at is abstraction (abstract mathematics, not like Java OOP). It allows expressive and efficient libraries to express AI algorithms. For example, HLearn uses well-known algebraic structures (modules, monoids, etc.) to express and boost the speed of simple machine learning algorithms.

Although you can write these algorithms in any language, Haskell makes them more expressive than others while maintaining decent performance. For instance, ‘faster cover trees‘ are written in Haskell.

Haskell supports embedded domain-specific languages, which is a famous area of programming language research applicable to a large number of domains, including artificial intelligence. Specifically, it is a good host for probabilistic programming and helps developers catch errors at compile time. If you are interested, you can check out Hakura, a research project creating embedded probabilistic programming.

The language has CUDA binding and is compiled to bytecode. Since it is functional and stateless, code can be easily executed on different CPUs in the cloud. If we talk about industry adoption, Facebook uses Haskell to fight spam.

6. JavaScript

A high-level, event-driven, interpreted programming language that is mostly used to make webpages interactive and create online programs and games.

In JavaScript, it’s not essential to learn a conversation model. Learn data on the server side and then call the learner by Ajax to predict. There are numerous libraries that make things easier for you. We are summarizing three of them –

• ConventJS: A library for implementing deep learning – train convolutional neural network in the browser. It supports fully connected layers as well as nonlinear neural network modules, classification, and regression cost functions.
• Synaptic: A neural network library for node.js. Its generalized algorithm is architecture-free, which lets you develop and train almost all types of first and second-order neural network architectures.
• Mind: It uses matrix implementation to process training data. You can completely customize the network topology and upload/download minds that have already been learned.

In short, you don’t have to reinvent the wheel – just determine what type of ‘learning’ the AI will do.

5. Prolog

Prolog is a logic programming language and semantic inference engine associated with computational linguistics and artificial intelligence. It has a flexible and powerful framework that is widely used for theorem proving, non-numerical programming, natural language processing, and AI in general.

It’s a declarative language with formal logic. AI developers value it for its pre-designed search mechanism, non-determinism, backtracking mechanism, recursive nature, high-level abstraction, and pattern matching.

Prolog is well suited for problems involving structured objects and relations between them. For instance, in Prolog, it is easier to express spatial relationships between objects, like a green triangle is located behind the blue one. It is also simple to state a general rule – if object A is closer to the person than object B, and B is closer than C, then A should be closer than C.

Prolog’s nature makes it simple and straightforward to implement facts and rules. If fact, everything in Prolog is a fact or a rule. It allows you to query the database even when you have thousands of these facts and rules.

Prolog supports the development of graphical user interfaces and administrative and network applications. It is well suited for projects like voice control systems and filling templates.

4. Java

Robotcode

The best thing about the Java language is its Java Virtual Machine technology, which allows you to create a single app version for all Java-supported platforms. Its strengths are transparency, maintainability, and portability.

What are the benefits of developing AI in Java? Well-supported large-scale projects, better user interaction, debugging ease, facilitated visualization, and incorporation of Swing and Standard Widget Toolkit.

The major advantage is its versatility – if you are a beginner, there are thousands of useful tutorials available on the internet (for free) that make your learning easier and more effective.

Some of the well-known applications developed in Java are

• WEKA machine learning suite, which is dedicated to machine learning and data mining
• JOONE neural engine for designing, training, and testing neural networks
• ALICE (short for artificial linguistic internet computer entity), natural language processing chatterbots
• Robocode, an open-source game for learning principles of Java programming

3. Lisp

Lisp is one of the oldest (developed in 1958) and most prominent languages created by Dr. John McCarthy, who coined the term ‘Artificial Intelligence.’ Although it is not used much these days, the language is both flexible and extendable.

It was originally developed for Lambda Calculus computation, and since its inception, it has evolved a lot. The language introduced many ideas in computer science, such as recursion, dynamic typing, higher-order functions, automatic storage management, self-hosting compilers, and tree data structures.

Lisp is used for developing Artificial Intelligence software because it supports the implementation of a program that works with symbols very well. Processing symbolic expression is what Lisp is good at.

Also, Lisp consists of a macro system, a well-developed compiler that can produce efficient code, and a library of collection types, including hashtables and dynamic-size lists.

There are thousands of AI applications developed in Lisp; popular names include-

• American Express Authorizer’s Assistant that checks transactions (credit card)
• METAL, a natural language translation system
• Macsyma, the first large computer algebra system
• ACL2, a theorem prover used by AMD

2. C++

C++ is faster than other languages – its ability to communicate at the hardware level allows you to improve code execution time. It is extremely useful for artificial intelligence projects that are time-sensitive. It can be used for statistical AI approaches like those found in neural networks.

With faster execution time and OOP principles, C++ makes itself a good candidate for AI programs. In fact, a vast portion of machine learning and deep learning libraries are written in C/C++.  

If you seek control over runtime and performance, C++ is an excellent choice. Its templates offer safer (type-safe) usage and provide an improved approach to generalizing APIs. While templates are a powerful tool that can simplify many aspects, determining when to use them effectively requires more time and experience.

The language overrides the complexities of 3D games, optimizing resource management and facilitating multiplayer with networking.

A real-world example is the science fiction game Doom 3, which uses C++ and the Unreal Engine, a suite of game development tools (written in C++). Microsoft Windows, Mac OS, Adobe Photoshop, Maya 3D software, CAD, and Mozilla Firefox are a few famous applications of C++.

1. Python

Python is focused on DRY (don’t repeat yourself) and RAD (rapid application development). Developed in the early 1990s, Python has become one of the fastest-growing programming languages because of its scalability, adaptability, and ease of learning.

Python has hundreds of libraries that support a wide range of projects, including mobile apps, web apps, data science, and artificial intelligence.

Notable examples include ‘Numpy’ for scientific computation, ‘Pybrain’ for machine learning, ‘Scipy’ for advanced computing, and ‘AIMA’ for artificial intelligence. These libraries enhance Python’s versatility and make it suitable for diverse applications.

Python’s holistic language design, the balance of low-level and high-level programming, modular programming, and testing frameworks make it different from other languages.

The other major benefit is fast prototyping. AI is about 80% research. In Python, almost all ideas can be quickly validated via 30-40 lines of code.

The list of Python applications is really long. It is used in image processing and graphic design programs, games, web frameworks, enterprise and business applications, and much more. Major websites like YouTube, Reddit, Quora, Dropbox, and Disqus are examples of successful platforms developed using Python.

Read: 50 Shades of Computer Programming Laws

Honorable Mentions

12. Smalltalk

A computer science luminary, he co-created Smalltalk, a language that paved the way for modern GUIs. Now, with Dynamicland, he’s reimagining computing’s future. #RoleModelsInCS pic.twitter.com/nnR6ik0kJo

— Code.org (@codeorg) January 21, 2024

Smalltalk features dynamic runtime typing methodology and maneuverability. In this language, there is no concept of integers, characters, and booleans — all these are treated as objects. In fact, classes and metaclasses are treated as objects.

These objects perform three major operations — receive messages from itself or other objects, send messages to itself and other objects, and reference other objects.

Since it has influenced several other languages (including Java, Python, C, and Ruby) and has an efficient computational power, it has been widely used in neural networks, machine learning, simulations, genetic algorithms, and numerous other industrial fields. Many web-developing firms use Smalltalk on a very large scale.

13. Wolfram Language

From species classification to protein visualization, just use Wolfram!
This collection of biology resources, projects and functions in Wolfram Language spans beginner to expert: https://t.co/SHPr6Zmr75 pic.twitter.com/6LXgGNhzyF

— Wolfram (@WolframResearch) January 22, 2024

Wolfram Language is a multi-paradigm programming language that focuses on rule-based programming, functional programming, and symbolic computation. It can employ arbitrary data and structures.

Wolfram makes it possible to express complex notions in computational form. To do this, it integrates high-level forms (such as Molecule, GeoPolygon, or Image) with advanced superfunctions (like ApplyReaction or ImageIdentify).

Overall, it represents everything (including documents, code, data, graphics, and interfaces) as symbolic expressions, making it easier to develop intelligent systems with any data type.

14. Artificial Intelligence Markup Language

With Artificial Intelligence taking over the world by storm, here are some Artificial Intelligence Markup Language Tags that you need to know.#skit #engineering #codinglife #aimlab #artificial_intelligence #aimltags #HTML #tags #coding #computerscience pic.twitter.com/JyjJRhbBhC

— Sri Krishna Institute Of Technology(SKIT) (@skit_org_in) May 9, 2023

AIML is an XML dialect meant to build artificial intelligence applications. It makes it easier to develop AI apps, while keeping the implementation easy to understand and highly maintainable. 

It contains a set of rules that define the conversational capabilities of the chatbot. The more rules we add in AIML, the smarter the chatbot is.

AIML interpreters are available in C#, Pascal, C++, Python, Java, Ruby, and other programming languages. Moreover, there’s a W3C XML Schema and a semi-formal specification for AIML to provide standardization.

More to Know

What are the top AI fields?

The five most demanding and high-paying AI fields are:

1. Machine learning
2. Data Science and Natural Language Processing
3. Robotics and Computer Vision
4. Reinforcement Learning and Generative Adversarial Networks
5. Business Intelligence System

The most popular AI projects

Hundreds of companies across the world have been working on AI for more than a decade. Some of the notable, non-classified AI projects are:

• Google Brain combines machine learning research with large-scale computing resources and information systems
• AlphaGo plays the Chinese board game Go
• PyTorch is an open-source deep learning framework known for its dynamic computational graph
• Libratus is designed to play Poker and intended to be generalizable to other systems
• Serenata de Amor is built to analyze public spending and identify discrepancies
• Melomics is an AI program for composing unique music instead of mimicking musicians
• ChatGPT is a chatbot fine-tuned with supervised and reinforcement learning methods

Machine learning market size

According to the Precedence Research report, the global market size of machine learning as a service will exceed $626.64 billion by 2032, growing at a CAGR of 39.3% from 2023 to 2032.

The major factor behind this growth includes the increasing demand for smart tools like facial recognition, data visualization, predictive analytics, and deep learning models. Moreover, many tech companies are strengthening their deep learning capabilities to drive innovations that are likely to drive machine learning market growth across regions and industries.

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