Latest research in Machine Learning Solutions for Cloud Security focuses on leveraging advanced ML algorithms to enhance threat detection, anomaly identification, and proactive defense mechanisms in cloud environments. Studies explore supervised, unsupervised, and deep learning techniques to identify malicious activities, unauthorized access, and abnormal network behavior in real-time. Research emphasizes integrating ML with intrusion detection systems, access control frameworks, and encryption-based solutions to improve scalability, accuracy, and response times in dynamic multi-tenant cloud infrastructures. Additionally, these approaches address challenges such as large-scale data processing, evolving attack patterns, and adaptive adversaries, aiming to strengthen the overall security, resilience, and trustworthiness of cloud computing platforms.