With the continuous growth of data scale and query complexity, traditional database systems exhibit significant redundant computation and resource contention when handling highly concurrent and diverse query workloads. Multi-query optimization (MQO) is a crucial approach for enhancing database performance. Its core principle is to identify and share overlapping computations among different queries to minimize repetitive execution and optimize resource utilization. Based on the timing and granularity of reuse, existing research is primarily divided into two technical routes: computation-reuse-based MQO and storage-reuse-based MQO. The former implements operator-level or plan-level sharing during query execution, while the latter achieves persistent reuse at the storage layer, also known as materialized view technology. With the advancement of artificial intelligence, reinforcement learning and deep learning methods are widely used for benefit estimation, plan generation, and sharing strategy decision-making, enabling the computation reuse process to achieve self-awareness, self-optimization, and self-evolution. This study systematically reviews the evolution from traditional methods to AI-enabled approaches, compares and summarizes the optimization objectives, decision mechanisms, and system architectures of various models, and analyzes the limitations of existing methods in terms of generality, scalability, and collaborative optimization. Finally, future research directions for intelligent and multi-level collaborative database optimization are envisioned.