AI tools, apps and tech have emerged exponentially in almost all the fields post-Covid pandemic, especially in the fields of medical sciences, robotics, process automation and academic research. From the developer’s perspective, there’s no one programming language for AI development that can be decided as the ultimate winner since all the good ones have their appeal. However, there is plenty of community support, documentation and tutorials available for aspiring or professional programmers to learn multiple languages to use for their specific tasks and sprints. According to, AI market size is expected to grow at a “CAGR of 31% during the forecast period. The “YOY (year-over-year) growth rate for 2021 is estimated at 27.61%” by the end of 2023.” Here’s our list of the most popular, reliable and preferred programming languages for AI development. 

List of Programming Languages for AI

  1. Python

Arguably the most popular and effective programming language when it comes to developing AI/ML, NLP development, Python is ideal for platform-agnostic programming as well as deep learning. Python code is simple, concise and readable, making coding more fun since thousands of algorithms AI/ML algorithms can be executed with the help of a huge library. More than half of the world’s developers prefer to use Python over other programming languages. Its libraries easily support object-oriented and procedural programming. 

Some of the best libraries on Python for AI/ML algorithm development are TensorFlow, SciKit-Learn, NLTK, among others. It’s the best for AI programming since it can also be used as a frontend language as well as soft computing, web scripting or ethical hacking.

  1. C++

The fastest programming language, C++ is ideal for being productive while developing stellar AI code for time-bound projects. Its extensive libraries are ideal for complex AI code, SEO optimization, ranking, faster mathematical calculations, and high-performance applications. While high-level programming can be better done in Python, intensive number-crunching can be done in a precompiled C/ C++. It’s also useful for multi-threaded programming. Some of its best libraries are Microsoft Cognitive Toolkit, mlpack Library, Dynamic Neural Network, Shogun, among others. For generalized APIs and safe templates, C++ is ideal for flexibility and increased output when combined with a hybrid of Python and other sprint specific tools. 

  1. R

Not only is R a great general-purpose language, but it is also a highly effective dynamically-typed, scripting, procedural programming language. It has something known as CRAN, a centralised repository of packages for general, special or highly specific tasks. R programming language IDE known as R Studio is a popular and essential part of the r-TensorFlow ecosystem. In the context of the new wave of AI/ML, R is especially useful for statistical computations, numerical analysis, visualisation, for numerical computation using data flow graphs. It enables programmers to train critical deep learning models with limited coding efforts. 

Widely used in finance, research and academia, R is ideal for AI modelling. 

  1. Prolog

A declarative programming language, Prolog is a semantic inference engine for logic programming in AI/ML, used for pattern matching over natural language parse trees. AI programmers can feed in the data like facts and rules concerning the end goal and you just query your data to execute logic programming. Even though it may not be considered a glamorous language, it’s widely used for pattern matching, Answer Set Programming or NLP, for simple and complex queries. It can generate code in Java, Ruby, C#, Objective-C, among others. Its compiler optimization can be implemented as a relation between an initial program and its optimized form. 

  1. Java

The most user-friendly programming language for object-oriented programming and scalability in AI projects along with the ease of simplified executions of data-intensive projects. It’s easy to maintain and transparent due to its Virtual Machine Technology. A large developer community supports coding all types of algorithms quickly and effectively. Some of its best features are simple debugging protocols, a built-in garbage collector and, swing and Standard Widget Toolkit. It wouldn’t be a stretch to say that Java is the most popular language preferred by most developers around the world. Java users perform better and faster while creating neural networks and search engine algorithms with concurrency. Its exceptional cross-platform ability can make it convenient to code for various platforms like Windows, Linux, Android, iOS. 

  1. LISP

It’s famous for quick AI prototyping, an exploratory macro system, compilers as opposed to interpreters and automatic memory manager, making it suitable for logic programming and machine learning. Since it’s one of the oldest programming languages alongside Fortran, it has helped develop R and Julia as well. It is highly capable of self-learning, self-modifying programs, and is quite flexible. The concept of functional programming was boosted by LISP and its program code is written as s-expressions or parenthesized lists. LISP development is done by Common Lisp and Scheme dialects. 

  1. Julia

A high-level and dynamic programming language, Julia offers native or non-native libraries to make AI/ML development highly performant. A type system with parametric polymorphism, numerical analysis, executing mathematics and scientific queries. It’s equally good for predictive modelling, statistics, visualisation and machine learning for complex projects. It’s easier than Python but ideal for scientific or complex computing in a variety of fields since it supports deep trained coding. It has a simpler syntax than Python and an astonishingly less percentage of CPU utilisation. Speed, productivity and scalability are the key features that make Julia one of the best languages for AI/ML development. 

  1. Haskell

A flexible language with great abstraction, Haskell is used by a select few among the developers who love to use Haskell for its abstract mathematics and expressive algorithms. It’s also perfect for applying advanced features like type classes, Glasgow Haskell Compiler, type polymorph, among others. 

As an AI enthusiast or developer, you could carefully weigh the pros and cons of each programming language as per the project requirements and use a convenient blend of languages that make you productive and innovative at the same time.