Intelligent AI Frameworks for Enterprise Cloud Infrastructure Optimization and Autonomous Workload Management

Enterprise cloud environments have become increasingly complex due to the rapid adoption of containers, microservices,serverless computing, distributed databases, and multi-cloud architectures. Conventional infrastructure management approachesdepend heavily on static configurations, predefined scaling rules, and manual intervention, which can result ininefficient resource utilization, excessive operational costs, performance degradation, and delayed responses to workloadchanges. Intelligent artificial intelligence frameworks offer an opportunity to transform cloud infrastructure managementfrom reactive automation into predictive, adaptive, and autonomous optimization. This research proposes an intelligentAI framework for enterprise cloud infrastructure optimization and autonomous workload management. The frameworkintegrates machine learning, reinforcement learning, predictive analytics, AI agents, cloud-native orchestration, observability,and policy-based automation to continuously monitor infrastructure conditions and make context-aware resourcemanagement decisions. The proposed methodology combines systematic literature analysis, framework design, prototypeimplementation, controlled workload experimentation, and quantitative evaluation. The framework is designed topredict workload demand, optimize compute and storage resources, perform intelligent workload placement, detectanomalies, balance performance and cost, and autonomously respond to infrastructure changes. Evaluation focuses onresource utilization, infrastructure cost, workload performance, energy efficiency, scaling accuracy, service-level objectivecompliance, recovery time, and autonomous decision accuracy. The research aims to establish a practical and measurableapproach for deploying intelligent cloud infrastructure optimization while maintaining enterprise security, governance,reliability, and human oversight.