Healthcare systems globally face increasing strain from aging populations, resource constraints and fragmented policy structures. In Europe, demographic aging is accelerating healthcare demand, exposing gaps in capacity and coordination. This study proposes a Multi-Agent System (MAS) framework to optimise resource allocation, policy response and inter-institutional coordination. By simulating hospitals, clinics, policymakers and patients as autonomous agents, the system supports real-time decision-making under conditions such as staff shortages, aging infrastructure and evolving care needs. Leveraging synthetic data generation and federated learning, the MAS is trained on diverse demographic scenarios - including rural-urban disparities and migratory pattern whilst preserving privacy. A scenario test demonstrates the model’s ability to reduce patient wait times by 35%, cut operational costs by 20% and improve bed turnover by 25% through adaptive agent coordination. Ethical safeguards ensure transparency and fairness, aligned with EU regulatory standards. The MAS offers a scalable, simulation-based tool for healthcare stakeholders to anticipate resource gaps, test policy interventions and strengthen systemic resilience in aging societies.