The growth and complexity of healthcare datasets, particularly within Medicare and Medicaid systems, exacerbate issues of interoperability, analytics, and integration with AI. To some extent, legacy data pipelines and extract-transform-load (ETL) frameworks have remained bounded by limitations on the expansiveness to address heterogeneous formats and regulatory compliance on standards such as HIPAA, HL7, and FHIR. To overcome these limitations, this research proposes a cloud-native data conversion framework using microservices, containerization, and serverless computing to build an AI-ready and scalable, secure foundation for healthcare analytics. The proposed framework allows for the raw ingestion of Medicare and Medicaid datasets with schema conversion automated into standard healthcare formats and optimized storage considerations downstream for analytics and AI. By benchmarking for performance, validating compliance, and testing for scalability, the framework gets to demonstrate superiority over traditional ETL pipelines regarding the speed of data conversion, resource elasticity, and integration with machine learning. Case studies exhibit its use in predictive care analytics, fraud detection, and healthcare policy optimization, establishing a determined path for real-time, data-driven decision-making within healthcare ecosystems. The work aligns with industry standards and leverages cloud-native advantages to contribute toward a scalable solution for transforming Medicare and Medicaid data into a foundation that fast-tracks advanced analytics and innovates AI-driven healthcare.