SQL injection remains one of the most prevalent and damaging security vulnerabilities in modern web applications. Traditional methods of detection often struggle to keep pace with the evolving sophistication of attack techniques. This research proposes a novel approach to SQL injection detection leveraging the power of reinforcement learning (RL). In this study, we develop a reinforcement learning-based system capable of effectively identifying SQL injection attacks with an impressive accuracy of 99%. The RL model is trained on a large dataset of both benign and malicious SQL queries, allowing it to learn complex patterns and behaviors indicative of attacks. By employing RL, our system continuously adapts and improves its detection capabilities, mitigating the challenges posed by dynamic attack strategies and evolving threat landscapes. The experimental results demonstrate the superior performance of our RL-based approach compared to traditional detection methods. Through rigorous evaluation on diverse datasets and real-world scenarios, we validate the robustness and effectiveness of our system in accurately detecting SQL injection attacks while minimizing false positives. Furthermore, we analyze the interpretability of the RL model to gain insights into the decision-making process behind detection, enhancing our understanding of attack patterns and aiding in the refinement of defensive strategies. Overall, this research contributes to the advancement of SQL injection detection methodologies, offering a highly accurate and adaptive solution that addresses the evolving challenges posed by malicious actors in web application security.