Impact of Artificial Intelligence and Machine Learning Techniques in Database Management System Components

The integration of artificial intelligence (AI) and machine learning (ML) in database management systems (DBMS) is revolutionizing how data is managed and analyzed. This review highlights the role of AI/ML in enhancing DBMS functions such as intelligent transaction scheduling (e.g., conflict prediction in OLTP), adaptive query optimization (e.g., reinforcement learning for plan selection), and automated configuration tuning (e.g., workload-aware systems like RocksDB). Key components include data preprocessing (feature extraction, normalization), architectural integration (embedding ML into optimizers, use of federated learning), and evaluation using metrics such as query latency, throughput, and model accuracy. While these innovations offer greater efficiency and scalability, ethical considerations such as data privacy, bias, and system transparency are crucial. This paper emphasizes the need for responsible AI adoption in DBMS to support real-time analytics, operational efficiency, and intelligent data-driven applications.