Improving interoperability in healthcare data systems is essential for enhancing clinical efficiency, patient outcomes, and system integration. Utilizing Machine Learning (ML) models on cloud platforms such as Pivotal Cloud Foundry provides a revolutionary method for facilitating smooth data interchange across diverse healthcare systems. Pivotal Cloud Foundry facilitates the modular deployment of machine learning algorithms that detect data discrepancies, standardize formats, and automate data mapping procedures via the use of microservices and containerization capabilities. These features diminish latency and enhance the precision of data transfer across electronic health records, diagnostic systems, and administrative databases. Machine learning improves interoperability by identifying trends, abnormalities, and missing values that often obstruct successful data integration. Pivotal's scalable architecture enables real-time processing, secure APIs, and comprehensive data governance, hence assuring regulatory compliance and uniform performance. The integration of cognitive computing and cloud-native infrastructure streamlines data harmonization and facilitates proactive healthcare delivery. The solution enhances operational efficiency, fosters increased cooperation among providers, and expedites the shift towards a cohesive healthcare data ecosystem characterized by more accurate, rapid, and accessible information sharing. © 2025 Elsevier B.V., All rights reserved.