This study investigates the transformative impact of artificial intelligence (AI) and machine learning (ML) on database management systems (DBMS), particularly in optimizing query execution, workload management, indexing, and security. This review delves into AI-driven methodologies, including reinforcement learning, deep learning, and Bayesian optimization, which significantly enhance scalability, automation, and efficiency within DBMS. Nonetheless, challenges such as high computational costs, integration complexities, and security vulnerabilities persist. The primary aim of this review is to evaluate current AI applications in database optimization, measure their effectiveness, and delineate future research trajectories. Findings indicate that AI-driven autonomous databases can substantially reduce manual interventions and enhance real-time adaptability. Future research endeavors should prioritize the development of energy-efficient AI models, explore federated learning for privacy preservation, and investigate quantum computing to overcome existing limitations and further propel the evolution of self-optimizing database architectures.
Othman et al. (Fri,) studied this question.