In order to provide scalability, agility, and dependability, database performance optimization is crucial as microservices architecture becomes the cornerstone of contemporary software development. Microservices decentralize data, which presents issues including consistency, latency, and distributed query execution, in contrast to monolithic systems. Polyglot persistence, indexing, caching, partitioning, CQRS, and event-driven synchronization are some of the tactics examined in this study to improve performance in these kinds of settings. In this paper Author examines the effects of different methods on system throughput, responsiveness, and resource usage, drawing on both academic research and real-world applications. Author also discusses emerging concepts like container orchestration, serverless databases, and AI-driven tuning. The results provide practical advice for creating scalable, robust, and effective data layers in microservices ecosystems.