Artificial Intelligence Driven Cloud Automation: Architecturing Intelligent Infrastructure Operations

Cloud infrastructure has grown too complex for traditional manual and rule-based management, leading to inefficiencies and scalability challenges. This paper proposes an AI-driven framework that combines machine learning, Infrastructure as Code (IaC), and AIOps within a microservices architecture to enable intelligent and autonomous infrastructure operations. Developed using the Rapid Application Development (RAD) methodology, the framework emphasizes adaptability, iterative refinement, and user-centered design. Experimental results show improved anomaly detection accuracy, faster incident response, reduced downtime, and lower operational costs compared to traditional management approaches. The work contributes a scalable blueprint that bridges theoretical AI models with practical enterprise cloud automation, paving the way for resilient, self-improving, and future-ready IT environments.