Chronic conditions are a critical challenge to health systems, and they need to be addressed in new ways. This paper discusses the creation of a machine learning-based drug recommender system intended to enhance patients’ adherence and maximize outcomes. It examines patients’ information including medical history, demographics, and treatment responses through the application of sophisticated algorithms and thus makes targeted drug recommendations. It has 92.5% accuracy and can potentially prevent drug-to-drug adverse interactions and aid in clinical decision-making. These results indicate the potential of the system in personalizing the treatment and involving the patient. The privacy issue of data, ethical issues, and interfacing with the existing healthcare system are also areas of concern. The current study is an example of how machine learning can transform the management of chronic diseases and how it requires additional studies to achieve its maximum potential for the betterment of healthcare delivery and outcomes.