The Artificial Intelligence (AI) and the semantic web data stores management have a dominant impact and potential, particularly through reinforcement learning (RL) applications to optimize Resource Description Framework (RDF) databases. This paper introduces dynamic optimization strategies for RDF databases, leveraging RL to enhance query performance, resource allocation, and adaptability in complex, evolving data environments. In this research, an examine of a variety of RL-based techniques applied to query execution, configuration tuning, and real-time resource management is achieved to address key challenges; in query optimization as well as in security, such as balancing exploration and exploitation, mitigating overfitting, and ensuring scalability and generalization. This research identifies optimal RL models and strategies that significantly improve RDF database efficiency by dynamically adjusting to workload changes and data patterns. The findings contribute to AI-driven database optimization, providing a scalable, adaptive framework applicable to diverse sectors, including cloud computing, the Internet of Things (IoT), and knowledge graph management.