Top Programming Languages to Design AI Apps In 2021

Nishi Agrawal
8 min readJun 17, 2021

AI is the ultimate choice for designing smart applications. Artificial Intelligence is changing the world around us and in this blog, we are going to discuss the top 9 programming languages for building AI applications in 2021. From Java to C++, Lisp and a few others, we have tried our best to include only those programming languages that have a higher level of popularity in the developer community.

NLP is becoming a very important part of our lives. Whether we are asking our smartphones for directions or engaging with Alexa or Google, Natural Language Processing is everywhere behind the scenes. NLP allows us to talk to machines and receive relevant output in the form of voice or text. It is a branch of AI that enables computers to understand, interpret and manipulate human language.

With the improvements and updates in technology, came the demand for skilled experts and engineers. There are plenty of programming languages to get you started with AI app development, but none of them is a one-stop solution. Specific objectives require specific approaches for every project.

With that, there are several programming languages in the market that enable developers to build futuristic AI applications. Let us discuss them one by one.

9 Best Programming Languages For AI App Development

For complete AI development, you need to be an expert in at least one of the many languages that are used for AI app development. There are several programming languages and most of them are available for free. Apart from that, you can also get access to video tutorials, helpful content, and the developer community in case you get stuck anywhere with a particular code string.

If you have no idea which language to start with, we are here to help you. Below are the top 9 programming languages along with their key benefits and unique features. So, let’s get started!


Python is one of the most popular programming languages used in Machine Language to date. It is a very powerful data analysis tool that is widely used in big data technology. Additionally, it is a high-level programming language that has several uses including internal web development and data science. It has earned a special status in the developer community because of the rapidly growing Artificial Intelligence industry.

These are some key features that make Python deserve the first rank in this blog

  • Easy-to-understand syntax
  • Tons of ready-to-use libraries from the Python developer community
  • Supports almost every operating system
  • Object-oriented approach
  • Access to any language stack as it is open-source

Here are the Python frameworks for AI app development

  • Theano
  • Scikit-learn
  • TensorFlow
  • Keras


LISP is widely used for in-depth scientific research in the field of natural languages. It is also used for analyzing theorems and AI issues. This programming language enables the developers to write complex code to process symbolic information. The main highlight of this programming language is that the data and programs are no different from each other within the development environment.

This enables the developers to write code that manipulates data and programs. This is the feature highlight that helps LISP bag second place in this listicle. It is undoubtedly one of the best programming languages for AI app development.

Let’s have a look at the features of LISP

  • This unique programming language allows the data structures to be executed as programs and these programs are modified as data.
  • Unlike the loop structure, in LISP, recursion is used as the main control structure
  • The best option for processing symbolic information
  • You can easily create new dynamic objects and develop prototypes according to your requirements.

Frameworks of LISP

  • Web locks
  • Clack
  • SabraOnTheHill


The R programming language dates back to the early 1900s and was a part of the GNU project. It has evolved with time and today it is widely used in data analysis for solving ML issues. This programming language is a great option for issues such as regression, decision-tree formation, classification, and many more. Just like Python, it is open-source, easy to install and configure.

R is the most preferred programming language of statisticians as it is widely used for processing and analyzing data. Developers can use R to create Artificial Intelligence projects based on mathematical symbols, graphs and formulas whenever required.

Features & benefits

  • This language effectively handles huge numbers of inputs and in some ways, it is better than Python
  • It allows working on both object-oriented programming and functional programming
  • It is independent and integrates well with the other programming languages
  • Apart from data analysis, this programming language is more geared towards data visualization

Frameworks of R

  • Keras
  • MXNet
  • TensorFlow


Prolog is another programming language developed in the 1970s. This language was used to provide logical conclusions from a given text. It is based on the first-order predicate theory wherein the interpreter continuously executes the output based on the logical specifications. This is one of the biggest advantages of using Prolog for Artificial Intelligence. Applying the first-order predicate theory to a programming language offers multiple features.

  • Well-defined semantics
  • Transparent and easy-to-navigate development environment
  • Easy syntax

Prolog is a pure implementation of programming language principles. It makes a significant contribution and helps in solving the issues of Artificial Intelligence. The most important feature is declarative semantics that helps the developer to express the relationships in AI operations. Apart from that, it also offers built-in unification tools, advanced search, and matching techniques.

Key benefits and important features of Prolog

  • Simple to use syntax and semantics
  • More powerful than Pascal and C (algorithmic languages)
  • The facts and rules can be dynamically changed by the program itself or by the user anytime


  • ProFL
  • Eclipse
  • SWI-Prolog


JavaScript was originally called Mocha and later was named LiveScript. Later in 1995, LiveScript was co-developed by Netscape and Sun Microsystems and was named JavaScript. Many features have been added to this powerful programming language since then.

Key benefits & important features

  • It has high computing performance that is accelerated with WebGL.
  • This programming language is known for being a secure development environment as it does not access your files.
  • The development, bug removal, and performance optimization process take place at higher speeds.


  • Tweety
  • Apache
  • Eye
  • PowerLoom


With the capabilities of both low and high-level programming languages, C++ offers a much higher level of control and efficiency than the other programming languages. The flexibility of the language is well-suited for resource extensive application and subsets of Machine Language programs. Apart from that, C++ is a static language and can perform different tasks at relatively higher speeds.

Developing Artificial Intelligence solutions in C++ is fast and easy because of its supreme programming capabilities. The library Boost offers robust mathematical processing capabilities and simplifies complex issues.

Key features & benefits

  • Simplifies and solves complicated AI issues
  • Offers a large set of library functions and tools
  • Being an OOP language, it helps in organizing the data


  • Boost
  • TensorFlow
  • Shark
  • Microsoft Cognitive Toolkit


Java is a high-level programming language that closely resembles C++ in structure. It offers a huge range of algorithms that are extremely useful for Machine Learning software development. Because of its bytecode and sandboxing, Java is one of the safest programming languages to develop AI applications.

Key features & important benefits

  • Simple bug-removal process
  • Easy-to-use interface and navigation
  • Effectively simplifies work on scalable projects
  • Easy graphical data presentation
  • User-friendly interface

JAVA incorporates both object-oriented and WORA patterns so it is the best choice to work on neural-network projects to develop search algorithms. JAVA is the easier path you can take to develop AI applications.


  • Hibernate
  • Blade
  • GWT
  • Spring
  • JSF


Scala offers a user-friendly development environment, robust security, and powerful tools. Additionally, this programming language also supports both functional and object-oriented programming. One of the best features of this language is the compiler. This tool can easily figure out the types for you. It also adds several functions to provide more features.

Functionalities provided in Scala

  • Improvements in the string-comparison
  • Pattern matching functionality
  • Mixins include functions in the class definitions

In this programming language, the developers can effectively process the data asynchronously and this helps in distributing the application.

Key features & benefits

  • Offers permanent data structure
  • Scala can be easily combined with Java stack
  • It offers a secure set of programming patterns

Scala offers great support and helps in real-time analytics projects. On top of that, it also offers a large open-source environment for statistical learning and information theory for Artificial Intelligence. Scala is a perfect choice if you want to develop larger AI apps.


  • Pinky
  • Play
  • Scalatra


Julia is quite new in the marketplace and is a dynamically compiled language focused on performance computing in scientific and technical fields. The combination of JIT in Julia has rendered it even more interactive and faster. The speed of computation can be compared with that of the programs written in the C language. JIT compilation in Julia is performed only when the code is executed and the functions are called dynamically during the run-time.

Key features of Julia

  • Random precision values
  • Common numeric data types
  • Set of mathematical functions
  • Code introspection and dictionaries
  • A built-in efficient package manager
  • Can interact with other languages

It also has a lot of high-level tools for parallel and distributed computing. Apart from that, it also supports metaprogramming.

Even though it is quite new, Julia is preferred more than other programming languages because

  • These packages are written in a high-level language
  • The packages are powerful software components that are easy to use no matter the language they are transmitted from.

Benefits of using Julia for AI app development

  • Simple to use and easy-to-understand syntax
  • JIT compilation
  • A large number of specialized packages
  • Faster services
  • Better performance

Julia is used for several purposes in AI including deep analysis and image processing, thematic modeling, classification of texts, and analysis of statistical models.


  • Merly
  • Bukdu
  • Genie

All In All

The Artificial Intelligence marketplace is growing and changing constantly. We witness it everywhere: in our offices, homes, in our cars and almost everywhere. Companies, regardless of their size and business are implementing AI solutions and deploying smart robots and intelligent answering machines using ready-to-use platforms. Different languages are appearing in the market and this is creating an era of competition. To keep things transparent and simple to understand, we created this listicle. Here, we have tried our best to include the top 9 programming languages that you should use to develop AI applications in 2021.

If you want to implement Artificial Intelligence in practice, or if you do not know which platform to choose, you can always rely on DRC Systems. Our AI consultants and data experts would love to help you identify your business requirements and select the right programming language for all your AI needs. Contact us right away!



Nishi Agrawal

Management Student, Digital Marketing Enthusiastic Interested in Web Security and Internet topics. Young Mind with creative thinking capabilities.