The key optimization technologies of traditional relational databases generally face bottlenecks including insufficient estimation accuracy, low efficiency of optimization decisions, and poor environmental adaptability when dealing with massive data processing, complex queries, and dynamic workload scenarios. Multi-granularity granular-ball computing provides a new solution for improving the performance of database systems, showing great research potential and application prospects. The core directions of artificial intelligence in driving intelligent optimization for databases are outlined, and the main challenges faced by existing learning-based optimization methods in terms of model generalization ability, interpretability, and handling complex queries and dynamic data distributions are explored. On this basis, the current status and key technologies of database optimization are systematically reviewed. Combined with multi-granularity granular-ball computing, database optimization techniques primarily focus on two aspects: query optimization and configuration optimization. For query optimization, the key techniques include cardinality estimation and join order selection. In terms of cardinality estimation, traditional methods are difficult to effectively support accurate estimation of complex joins and nested queries involving multiple tables, and often result in huge storage overhead. Learning-based methods can better handle high-dimensional data relationships, and a new method that uses multi-granularity granular-ball computing techniques to extract hierarchical distribution features of data and combines them with tree-structured neural networks is introduced, which can significantly improve the robustness and accuracy of complex query cardinality estimation. In terms of join order selection, traditional methods have low search efficiency in multi-table join scenarios. Static methods based on historical experience learning have limited adaptability to new query patterns. Although dynamic learning methods can support runtime adjustments, they incur a high cost. In contrast, representing the join plan as a combination of multi-granularity granular-balls with geometric relationships, optimizing the search space through a hierarchical structure, and integrating deep reinforcement learning for decision-making provides an efficient approach to finding a globally approximate optimal join order. For database configuration optimization, parameter tuning plays a key role in improving performance. Traditional optimization techniques based on search are difficult to obtain the global optimal solution within a reasonable time. Although traditional machine learning methods can achieve automated tuning, they highly rely on the quality and coverage of training data. Reinforcement learning methods continuously improve strategies by interacting with the system environment, requiring only a small number of samples to achieve strong adaptive parameter tuning. By integrating multi-granularity granular-ball computing methods, the characteristics of the parameter space can be efficiently represented, thus significantly improving tuning efficiency and effectiveness. Although the prospects of optimizing intelligent databases using granular-ball computing techniques are broad, practical applications still face major challenges such as effective integration with existing database models, reduction of computational overhead, and ensuring model stability under dynamic workload changes. Future research requires continuous development of theories and technologies to promote database systems toward more intelligent, efficient, and robust directions.