In healthcare, ensuring the integrity and accuracy of scientific information is paramount. This undertaking provides a progressive framework for mechanically detecting and correcting statistical impurities in healthcare datasets through reinforcement, gaining knowledge of techniques, and significantly leveraging the Q-learning algorithm. The framework begins by pre-processing unplanned healthcare statistics and identifying commonplace impurities, lacking values, outliers, and inconsistencies. Subsequently, a Q-mastering agent is deployed to examine the most reliable records correction techniques autonomously through trial-and-error interactions with the dataset. Through a sequence of iterations, the agent refines its choice-making method, prioritizing corrections based on the severity and impact of the detected impurities. This adaptive approach allows for dynamic modifications to varying statistical traits and complexities, ensuring robustness across diverse healthcare datasets. Experimental reviews exhibit the efficacy and scalability of the proposed framework in efficaciously identifying and rectifying facts and impurities with minimal human intervention. Comparative analyses against traditional rule-based strategies underscore the prevalence of the reinforcement getting to know technique in managing complex impurity patterns and achieving higher accuracy costs. The results of this research offer significant implications for reinforcing the first-class reliability of healthcare facts, which, in the long run, contributes to more informed choice-making techniques and improved affected person outcomes