Machine Learning for Predictive Cloud Management Revolutionizing IT Monitoring and Maintenance

As cloud infrastructure becomes the backbone of modern digital services, maintaining performance, availability, and scalability has become a critical challenge. Traditional reactive monitoring approaches often fall short in dynamic cloud environments, where real-time decision-making and foresight are essential. This paper explores the transformative role of machine learning (ML) in predictive cloud management, focusing on how intelligent algorithms can anticipate system anomalies, optimize resource allocation, and reduce downtime. By integrating supervised and unsupervised learning models into IT operations, organizations can shift from a reactive to a proactive stance, enhancing efficiency and service reliability. The study presents a review of current ML techniques applied in cloud monitoring, examines their practical applications in anomaly detection, workload forecasting, and auto-scaling, and highlights the evolving landscape of AIOps. Furthermore, it addresses the challenges in model interpretability, data quality, and integration with legacy systems, offering a forward-looking perspective on the future of autonomous cloud infrastructure. The findings underscore the potential of machine learning not only to automate but also intelligently manage complex cloud environments, ushering in a new era of self-healing, self-optimizing IT operations.