Efficient query optimization is essential for reducing execution latency and improving performance in large-scale databases. Traditional optimization techniques such as Cost-Based Optimization (CBO), Rule-Based Optimization (RBO), indexing, and execution plan tuning have been widely used in relational, NoSQL, and distributed database systems. However, as database sizes grow exponentially and query complexity increases, these conventional approaches face challenges in handling high-latency queries, optimizing resource consumption, and adapting to dynamic workloads. This research aims to conduct an empirical analysis of query optimization strategies, comparing traditional methods with AI-driven techniques across various database architectures, including SQL, NoSQL, and distributed cloud databases.

Through a series of experimental evaluations using benchmark datasets (TPC-H, TPC-DS) and real-world query workloads, we demonstrate that AI-enhanced query optimization techniques significantly improve query execution latency (up to 45% reduction), CPU usage, and disk I/O efficiency. Additionally, we propose a hybrid optimization framework integrating Machine Learning-Based Query Optimization, Adaptive Query Execution, and Query Execution Caching, which shows superior performance in distributed and large-scale environments. The findings provide valuable insights into the role of AI in modern database optimization, offering a roadmap for future research in AI-driven query execution strategies and automated workload management.