REINFORCEMENT LEARNING–DRIVEN CLOUD MIGRATION AND FINOPS OPTIMIZATION USING PREDICTIVE AND AUTONOMOUS DECISION INTELLIGENCE

Artificial intelligence has increasingly been explored as a mechanism for improving cloud planning, migration, and operational efficiency.Earlier studies such as "Artificial Intelligence-Based Cloud Planning and Migration to Cut the Cost of Cloud" and "Leveraging Artificial Intelligence for Smart Cloud Migration, Reducing Cost and Enhancing Efficiency" demonstrated that machine learning, natural language processing, and heuristic decision logic can improve migration planning and reduce cloud-related expenditure.While these works established the feasibility of AI-assisted migration, they primarily focused on application-level optimization and conceptual automation.This paper (Version 3 -Advanced, Continuity Explicit) advances the research trajectory by proposing a next-generation, reinforcement learning-driven architecture for autonomous cloud migration and continuous FinOps optimization.The proposed framework introduces a predictive and self-optimizing decision intelligence layer that dynamically learns optimal migration strategies, resource allocation policies, and costgovernance controls under changing workload, pricing, and performance conditions.