Traditional Database Management Systems are under increasing pressure to effectively manage, process, and extract value from large and complex datasets due to the recent rapid expansion in data. Artificial Intelligence integration with database management systems has emerged as a viable option as enterprises grapple with the issues of data scalability, efficiency, and security. In order to improve a number of database tasks, such as query optimization, automated data management, anomaly detection, and system security, artificial intelligence techniques like machine learning, the processing of natural languages, expert systems, and deep learning are being investigated. By examining how AI approaches overcome the drawbacks of traditional database structures and improve operational efficiency, this study explores the influence of AI on contemporary DBMS. The study additionally examines at AI's ability to recognize abnormalities in database operations, identifying possible security lapses and strange data access trends that conventional techniques might miss. The study presents real-world AI applications in DBMS applications, including cloud databases, big data systems, and distributed database environments, through detailed case studies.

Artificial Intelligence, Database Management Systems, Machine Learning, Query Optimization, Natural Language Pro-cessing, Data Security, Data Cleaning