SOVEREIGN CLOUD AUTONOMY FEDERATED ARCHITECTURE FOR POLICY-COMPLIANT, ENERGY AWARE, AND AI-OPTIMIZED MULTI REGION CLOUD WORKLOAD MANAGEMENT AIMING TOWARD INTENT-DRIVEN CLOUD COMPUTING: A SELF ADAPTIVE FRAMEWORK FOR EDGE-AWARE RESOURCE SCALING, COMPLI

The evolution of cloud computing demands architectural models that incorporate sovereignty, energy efficiency, and AI-driven automation. This paper proposes a federated sovereign cloud autonomy architecture that supports multi-region workload management with an emphasis on policy compliance, energy awareness, and intelligent orchestration. Through a self-adaptive intent-driven framework, the system enables dynamic edge-aware resource scaling, thereby balancing sustainability and service availability. This research also integrates reinforcement learning-based optimization and regulatory-aware scheduling mechanisms to support intelligent decision-making in resource-constrained and diverse geopolitical cloud environments.