AI-Powered Database Management: Predictive Analytics for Performance Tuning

As data volumes and query complexities increase in modern applications, maintaining optimal database performance has become difficult using traditional, manual, and largely reactive tuning techniques. This paper investigates how Artificial Intelligence (AI) and predictive analytics can be integrated into Database Management Systems (DBMS) to enable proactive, automated performance tuning. It reviews recent AI- and Machine Learning (ML)-based methods such as regression models, anomaly detection, time-series forecasting, reinforcement learning, and AI-driven query optimization and index selection. A mixed-method approach is used, combining a structured literature review (2019–2025) with comparative analysis of empirical studies focusing on metrics like accuracy, latency reduction, throughput, and scalability. Based on synthesized findings, the paper proposes an enhanced AI-driven framework for real-time query optimization, predictive maintenance, and dynamic resource allocation in DBMS. Experimental results from the surveyed works indicate substantial improvements in query execution time, throughput, and system responsiveness, suggesting that predictive analytics can significantly increase DBMS efficiency. The paper also addresses ethical considerations, computational overhead, interpretability, and integration challenges, and outlines recommendations and directions for future research toward autonomous database tuning in cloud and enterprise environments.