Efficient bed allocation and smooth patient flow are critical for maintaining high-quality hospital operations, especially under fluctuating patient demand and resource constraints. Traditional centralized decision-making systems often lack the adaptability, interpretability, and scalability required to address these challenges in real time. Our work presents a conceptual Multi-Agent System (MAS) framework designed specifically for hospital bed allocation and patient flow optimization. The framework incorporates specialized agents, such as Bed Availability Agents, Patient Prioritization Agents, Transfer Coordination Agents, and Resource Forecasting Agents that work collaboratively via standardized communication protocols. First, we present the system architecture, coordination strategies, and decision-making workflows that facilitate decentralized yet synchronized operations, ensuring patient safety, adherence to medical protocols, and transparent hospital management. The approach emphasizes modularity and explainability, enabling integration of diverse predictive models, real-time hospital data, and clinical guidelines into a unified decision-support environment. Then we demonstrate the framework’s applicability through simulated hospital scenarios, showcasing its potential to improve occupancy rates, reduce patient wait times, and support surge capacity management. Our work provides a foundation for developing adaptive, trustworthy, and scalable AI-based hospital resource management systems.