In today’s data-driven world, organizations face constant pressure to extract meaningful insights from complex databases while maintaining fast, responsive applications, making query optimization a critical challenge for database professionals. This research explores how two powerful techniques—index merge strategies and caching—can work together to dramatically improve MySQL query performance in real-world e-commerce scenarios. We developed and tested practical Python solutions: an intelligent LRU caching system for rapid retrieval of frequent queries (ideal for read-heavy analytics workloads), and an index merge optimization framework leveraging MySQL’s cost-based optimizer to handle complex multi-filter queries (like finding customers by location, date range, and product category). Testing on an e-commerce database with customers, orders, products, and reviews revealed significant performance gains—caching delivered near-instant responses for repeated queries while index merges excelled at complex multi-table filtering—though both approaches involve tradeoffs like increased memory usage and optimization complexity. By providing ready-to-use Python implementations, we bridge theory and practice, offering database teams actionable ways to optimize performance in production environments where milliseconds matter. Our findings demonstrate how strategic tuning of these techniques can help modern databases meet today’s demanding speed and efficiency requirements while handling increasingly sophisticated queries.