Research on Machine Learning Methods for Cloud Computing focuses on leveraging intelligent algorithms to enhance cloud performance, automation, and decision-making across various operational layers. This area explores how supervised, unsupervised, and reinforcement learning techniques can optimize resource allocation, task scheduling, fault prediction, and energy efficiency. Key research directions include predictive analytics for workload forecasting, anomaly detection in cloud operations, intelligent auto-scaling mechanisms, and adaptive load balancing using ML models. Other emerging topics involve applying deep learning for network optimization, reinforcement learning for dynamic resource management, and federated learning for privacy-preserving cloud intelligence. Additionally, research on explainable AI for transparent cloud decision-making, ML-driven security threat detection, and integrating edge intelligence for distributed cloud learning environments are gaining significant attention in advancing autonomous and efficient cloud ecosystems.