Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The exponential expansion of digital ecosystems has presented hitherto unheard-of problems with infrastructure maintenance, therefore driving artificial intelligence to maximize cloud activities. A paradigm shift marks AIOps from conventional reactive monitoring to proactive, intelligent system management. Using machine learning models now allows predictive autoscaling through reinforcement learning techniques that project workload patterns and dynamically change compute resources. While sophisticated root cause analysis offers contextual insights from performance metrics and log data, advanced anomaly detection systems filter monitoring noise to spot real service degradations. By dynamically balancing cost, performance, and availability concerns, AI-driven capacity planning maximizes resource consumption throughout hybrid and multicloud settings. Open source systems like Kubeflow and exclusive innovations from major cloud providers propel invention in this industry. As AIOps evolves from experimental technology to operational need, companies employing AI-powered infrastructure have great advantages in resilience, cost effectiveness, and scalability. This transformation reflects the incorporation of adaptive intelligence rather than only automation, matching infrastructure performance with shifting company objectives.