Abstract: The integration of business analytics in hospital management is revolutionizing decision-making processes, enhancing operational efficiency, and improving patient outcomes. This study explores the impact of predictive modeling, real-time analytics, and AI-driven decision support systems on hospital operations, financial sustainability, and healthcare delivery. By adopting a mixed-methods approach, the research examines how business analytics optimizes resource allocation, reduces hospital readmissions, and enhances cost efficiency. The findings suggest that predictive analytics enables early disease detection and personalized treatment plans, while real-time analytics improves hospital workflow by dynamically monitoring patient admissions and staff deployment. AI-driven systems further contribute to financial management by identifying unnecessary expenditures and streamlining administrative tasks. However, the study also highlights key challenges, including data interoperability issues, privacy concerns, and resistance to AI adoption among healthcare professionals. The research emphasizes the need for standardized data-sharing frameworks, stronger security protocols, and targeted staff training programs to facilitate the effective implementation of business analytics in hospital management. Future research should focus on developing cost-effective solutions for smaller healthcare facilities and engaging policymakers in regulatory frameworks that promote the ethical use of business analytics in hospitals. By addressing these challenges, hospitals can fully leverage data-driven decision-making to enhance efficiency, reduce costs, and improve patient care. This study contributes to the growing body of knowledge on business analytics in healthcare, providing practical insights for hospital administrators, policymakers, and technology developers seeking to enhance hospital management through data-driven strategies.