AI-Powered Multi-Cloud Data Optimizer for Healthcare: Architecture, Algorithms, and Comparative Performance Evaluation

Healthcare workloads growing in their data requirements demand efficient cross-platform management because of this need. This paper describes the new AI-powered multi-cloud Data Optimizer (MC-DO) that distributes healthcare data alongside computing responsibilities between AWS, Azure, and GCP using intelligent management systems. The MC-DO provides edge computing abilities alongside AI accelerators and real-time work-responsibility prediction algorithms, which differentiate it from basic multi-cloud solutions. The paper describes how the MC-DO hardware operates along with its processing design and orchestration procedures. The experimental results show how MC-DO minimizes delivery times while reducing costs and enhancing resource consumption, and solution models, analytics, and comparison datasets support these findings.