Recent research in machine learning methods for cloud computing emphasizes the use of intelligent algorithms to optimize resource management, task scheduling, performance prediction, and security enhancement in dynamic cloud environments. Machine learning techniques such as deep learning, reinforcement learning, and ensemble models are widely applied to predict workloads, detect anomalies, automate resource scaling, and improve energy efficiency. These approaches enable adaptive and data-driven decision-making that enhances Quality of Service, reduces operational costs, and ensures system reliability. By integrating machine learning into cloud infrastructures, researchers aim to build self-optimizing, efficient, and resilient cloud systems capable of handling complex and large-scale computational demands.