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To offer the patients' medical care and successful outcomes, a successful hospital management is required. Traditional hospital management systems, which typically rely on manual procedures, are unable to handle the vast amount of data that is generated in the field of health in an effective manner. The statistical management method for healthcare systems that we discuss in this paper is based on machine learning. A significant amount of hospital-related data, including patient records, medical procedures, staffing levels, and resource distribution, are evaluated using the methodology, which makes use of automatic learning capabilities. The paper's objective is to create predictive models that, through patient demand projection, resource optimization, and resource improvement, assist hospital management in making better decisions. To correctly forecast future patient loads, bed occupancy rates, and staffing requirements, analyze previous data to identify patterns and trends. These predictions can assist hospital administrators in efficiently allocating resources, cutting down on patient wait times, and raising patient satisfaction. Additionally, our approach gives hospital administrators dashboards and visualizations that provide crucial performance indicators, merging data-driven decision-making, so they can monitor and assess the effectiveness of their management strategies. Utilizing automatic learning algorithms ensures the adaptability and scalability of the hospital management system. [ABSTRACT FROM AUTHOR]