To address significant issues in healthcare data exchange and analysis, this research paper investigates the integration of Federated Explainable Artificial Intelligence (Fed XAI) with blockchain technology. Fed XAI is a cutting-edge method that blends Explainable AI (XAI) approaches with Federated Learning (Fed) privacy-preserving features to enable collaborative model training without jeopardising the confidentiality of sensitive patient data. Participating healthcare organisations share model updates or gradients rather than raw data, assuring data security. The ecosystem is improved using blockchain technology since it provides data integrity, transparency and immutability. The blockchain’s decentralisation streamlines patient care and administrative procedures by enabling seamless data interchange and interoperability among healthcare providers. By offering concise and understandable justifications for the actions taken by AI models, explainable AI empowers healthcare providers. This encourages confidence and trust in AI-driven recommendations, enhancing clinical decision-making. Healthcare organisations are encouraged to submit data through token-based incentives, fostering collaboration and improving model precision and generalizability. The potential for combining Fed XAI and blockchain in healthcare data exchange and analysis is highlighted in this research paper. To fully realise the promise of this game-changing solution, which will ultimately improve patient care and healthcare efficiency, collaboration efforts across healthcare organisations, AI developers and blockchain experts are essential.