Türkmen,G.Sezen,A.Şengül,G.Computer Engineering2024-10-062024-10-06202402149-914410.22399/ijcesen.3422-s2.0-85203684501https://doi.org/10.22399/ijcesen.342https://hdl.handle.net/20.500.14411/9579This study presents a detailed comparative analysis of the foremost programming languages employed in Artificial Intelligence (AI) applications: Python, R, Java, and Julia. These languages are analysed for their performance, features, ease of use, scalability, library support, and their applicability to various AI tasks such as machine learning, data analysis, and scientific computing. Each language is evaluated based on syntax and readability, execution speed, library ecosystem, and integration with external tools. The analysis incorporates a use case of code writing for a linear regression task. The aim of this research is to guide AI practitioners, researchers, and developers in choosing the most appropriate programming language for their specific needs, optimizing both the development process and the performance of AI applications. The findings also highlight the ongoing evolution and community support for these languages, influencing long-term sustainability and adaptability in the rapidly advancing field of AI. This comparative assessment contributes to a deeper understanding of how programming languages can enhance or constrain the development and implementation of AI technologies. © IJCESEN.eninfo:eu-repo/semantics/closedAccessAIMachine Learning ApplicationsProgramming LanguagesArticleN/AQ4103461469