The transition to Healthcare 5.0—marked by the convergence of artificial intelligence (AI), cyber-physical systems, and real-time data connectivity—promises to usher in a new era of personalized, predictive, and intelligent patient care. However, realizing this vision poses major challenges, especially in safeguarding data privacy, ensuring institutional trust, and securing decentralized systems. Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving AI by enabling collaborative model development without centralizing sensitive medical data. Despite its potential, FL’s adoption in healthcare is hindered by a persistent “trust deficit” among stakeholders, rooted in concerns over auditability, system transparency, and susceptibility to adversarial threats such as model poisoning and data falsification. This chapter explores the integration of blockchain technology with Federated Learning as a comprehensive solution to these issues. Blockchain’s decentralized and immutable architecture, combined with smart contract automation, provides the necessary infrastructure to enhance trust, secure aggregation processes, and enable transparent, tamper-proof audit trails. We present architectural frameworks that utilize hybrid on-chain/off-chain models, incorporate zero-knowledge proofs (ZKPs) for enhanced privacy, and leverage self-sovereign identity (SSI) systems for secure participant authentication and data control. Through case studies in drug discovery, clinical trials, and pandemic response networks, we illustrate how this synergy not only mitigates data and security risks but also facilitates real-time regulatory compliance with global standards such as GDPR and HIPAA. Ultimately, the fusion of blockchain and Federated Learning presents a scalable, trustworthy foundation for the next generation of healthcare AI, enabling secure collaboration across institutional and national boundaries while preserving the core ethical principles of medical data stewardship.