Future of Data Sorting: Integrating AI for Enhanced Efficiency and Accuracy

Abstract: Sorting is a fundamental operation in computer science, critical for data organization, retrieval, and analysis. Traditional algorithms like QuickSort and MergeSort excel in smaller datasets but struggle in big data contexts, prompting the need for innovative solutions. Artificial Intelligence (AI) has emerged as a transformative force, enabling adaptive, intelligent sorting mechanisms that significantly outperform conventional methods in efficiency and scalability. This paper examines the integration of AI into sorting algorithms, highlighting innovative approaches, comparative analyses, and implications for big data. By exploring machine learning, neural networks, and genetic algorithms, this research reveals the potential of AI-driven sorting to enhance performance in diverse environments. The findings emphasize the importance of hybrid AI-traditional models for addressing computational costs and scalability challenges, underscoring the need for continued research.