A Reinforcement Learning, a pivotal component of artificial intelligence, is employed by computers to learn intelligently. This study delves into the application of a significant mathematical concept, the Markov Decision Process (MDP), within the realm of Reinforcement Learning. The primary focus lies in the development of a proficient computer program designed to tackle healthcare issues. The proposed approach consists of two key elements: a unique decision-making framework and intelligent learning mechanisms. This process of combining two elements is leveraged to analyze patient information and ascertain optimal choices. Conceptualizing a patient’s healthcare journey as distinct states—such as physician visits (O), hospitalization (H), intensive care (I), or mortality (D)—the research formulates a Markov Chain Model. This model quantifies the transition probabilities between these states. Additionally, an auxiliary model is constructed to gauge the efficacy of decisions, encompassing factors like risk assessment and potential medication outcomes. The effectiveness of the proposed model, termed the Markov Model with Reinforcement Learning, is evaluated using real-world patient data from electronic health records. Encouragingly, the model demonstrates proficiency in predicting forthcoming healthcare events. This underscores its utility in prognosticating future developments within the healthcare domain.