The healthcare industry harnesses machine learning techniques in the prediction of diseases, hospitalization rates, and medical cost estimates. Advanced technologies in health care provide solutions by analysis of the medical data extracted through data mining. In this regard, predictive models could be developed that will estimate recurring diseases, prescribe relevant medication, and suggest hospitals for admission and specialist consultations that will be aligned with the profile of individual patients. Machine learning is very important in predicting hospitalization and estimating the cost toward health care by analyzing patients’ demographic data and disease. This includes, but is not limited to, severity and physiological markers such as blood pressure. By utilizing previous patient data and predictive analytics, hospital admission risk, stay length, and medical expenditures can be predicted and help in recommending treatment plans if hospitalization is required with much accuracy by machine learning algorithms. This ability to predict will make health providers create informed decisions, and improve treatment planning strategies and patient management strategies. Further, by implementing ensemble technique to improve the performance over linear models and provide insights into the customization of the dietary requirements during hospitalization, hence settling for a comprehensive patient receptive plan.