This research investigates the intricate relationship between library optimization and machine learning algorithm performance across Python, Java, C++, and Julia. Through comprehensive benchmarking of widely used libraries, the study reveals that library efficiency often supersedes the inherent characteristics of programming languages in determining execution speed, accuracy, and energy consumption of machine learning models. The findings challenge the conventional wisdom that compiled languages invariably outperform interpreted ones in computational tasks. Notably, Python’s well-optimized libraries, such as Scikit-learn, demonstrate competitive and sometimes superior performance compared to C++ implementations in specific scenarios. This paradigm shift underscores the critical importance of library selection over language choice in optimizing machine learning workflows. The study delves into the nuanced interplay of factors influencing machine learning performance, including execution efficiency, ecosystem richness, and implementation ease. It also examines the impact of Just-In-Time (JIT) compilation in Julia, revealing significant performance enhancements in subsequent runs, which points to its potential in long-running or repetitive tasks. By providing a comprehensive analysis of the performance landscape across different programming languages and libraries, this study offers valuable insights for practitioners and researchers. It enables informed decision-making in selecting optimal tools and languages for specific machine learning applications, considering not only computational efficiency but also broader ecosystem factors and long-term maintainability. Ultimately, this research contributes to a more nuanced understanding of the performance dynamics in machine learning implementations, challenging preconceptions and providing a data-driven foundation for optimizing machine learning workflows across diverse computational environments.