This study examines how artificial intelligence (AI) and advanced analytics can transform healthcare operations by bridging clinical insight with operational efficiency. It synthesizes evidence on predictive and prescriptive modeling, workflow optimization, resource allocation, and governance frameworks to demonstrate how AI-enabled platforms enhance both clinical outcomes and system performance. Core findings indicate that integrating multi-source clinical and operational data, interpretable machine learning models, and real-time decision support enables accurate risk stratification, improved patient flow, and optimized resource utilization. Frameworks illustrate the alignment of predictive insights with operational objectives, while ethical, regulatory, and organizational enablers, including standardized architectures, federated learning, and cross-functional governance, facilitate enterprise-wide adoption. Therefore, AI and analytics platforms are positioned as foundational enablers of modern healthcare systems, empowering proactive risk management, value-based care delivery, and resilient, patient-centered operations in resource-constrained environments.