Efficient resource allocation is a critical challenge in hospital operations, directly impacting patient care, operational costs, and overall healthcare efficiency. This study explores the integration of Artificial Intelligence (AI) in optimizing hospital resource allocation through advanced machine learning algorithms and predictive analytics. AI-driven tools enhance decision-making by analyzing patient flow, predicting demand for medical resources, and automating staff and equipment scheduling. By leveraging real-time data, these tools enable dynamic resource distribution, reducing bottlenecks, minimizing patient wait times, and improving overall hospital efficiency. This paper reviews current AI applications in hospital resource management, including machine learning models for demand forecasting, reinforcement learning for adaptive scheduling, and optimization algorithms for supply chain management. Case studies demonstrate AI’s impact on reducing emergency department congestion, enhancing staff workload distribution, and optimizing bed occupancy. Challenges such as data privacy concerns, integration with existing hospital information systems, and model interpretability are also discussed. Findings indicate that AI-driven resource allocation significantly enhances hospital operations, leading to improved patient outcomes and cost savings. The study highlights the need for further research on AI adoption strategies and regulatory considerations to ensure seamless integration into healthcare systems. AI’s potential in hospital management presents a transformative opportunity for the future of healthcare.