Relational Database Management Systems (RDBMS) play a critical role in storing and managing sensitive data across industries such as finance, healthcare, and government. However, traditional static security mechanisms, including Role-Based Access Control (RBAC), encryption, and Intrusion. Detection Systems (IDS), often fall short in addressing the dynamic and sophisticated nature of modern cyber threats. This paper introduces an innovative integrated framework that combines Metaheuristics, Reinforcement Learning (RL), and Triple Loop Learning (TLL) to enhance the security of RDBMS environments. Metaheuristics optimize critical security configurations, such as access control policies and encryption strategies, ensuring an effective balance between security, performance, and resource utilization. RL adapts to emerging threats in real time by continuously learning from system feedback, dynamically adjusting security policies to counter Advanced Persistent Threats (APTs), insider attacks, and zero-day vulnerabilities. TLL enables the strategic evolution of security measures, ensuring compliance with regulatory frameworks such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). The extended framework presented in this paper highlights significant contributions, including a trade-off analysis between security and system performance and practical applications validated through real-world case studies. These case studies demonstrate the effectiveness of the framework in reducing unauthorized access, improving intrusion detection accuracy, and aligning security strategies with organizational and regulatory goals. By leveraging the synergy between Metaheuristics, RL, and TLL, this research offers a scalable, adaptive, and forward-thinking approach to securing RDBMS environments against evolving cyber threats.