Reinforcement learning (RL), which makes use of artificial intelligence techniques, has emerged as a viable method for treating heart illness and improving general health. Since RL algorithms enable agents to learn the optimum decision-making principles through interactions with the environment, they are highly suited for personalized health recommendations and therapy optimization in the context of heart disease. We first look into how RL algorithms may utilize patient-specific information, such as medical history, lifestyle decisions, genetic factors, and physiological markers, in order to provide tailored health advice. By continuously learning from patient feedback, RL agents may adjust and provide tailored therapies including exercise regimens, dietary adjustments, medication adherence measures, and stress management techniques. Plans for treating heart disease can also be enhanced via reinforcement learning. Medical professionals can also use RL-based decision support systems to assist in the treatment of heart illness. By reviewing a vast amount of medical literature, treatment suggestions, and patient outcomes, RL algorithms such as Markov Decision Process may suggest evidence-based therapies and help accurate diagnosis. However, the successful integration of RL into general health and heart disease includes that in the event of a doctor's availability, the medications will be provided and the remainder of the pills will be raised according to the time, and in the event that a doctor is not present, this provides virtual contact with the doctor, who will then provide the medications along with the pills remainder.