The acceleration of technology integration in healthcare has made it more difficult to ‘balance’ data access and patient confidentiality during clinical operations and research. Legacy Master Data Management (MDM) approaches promise a high level of data integrity and reliability (the golden record) while lacking in addressing privacy and compliance needs. Also, centralized AI healthcare analytics models pose serious data leakage and compliance risks. This paper presents a privacy by design approach of Fusion AI (Federated AI) with MDM for these use cases. Using federated learning, sensitive patient data is kept within healthcare institutions while complying with global regulations like HIPAA and GDPR during training of shared global AI models. The case with advanced MDM with federated AI illustrates improved data quality and patient privacy which support interoperability within distributed federated healthcare ecosystems. This paper provides a conceptual architecture and scenario-driven analysis of how MDM with federated AI enhances compliance and patient trust while compliance-enabled MDM accelerates innovation and fosters scalable privacy-respecting governance of healthcare data.