The rapid-fire integration of cloud computing and machine literacy (ML) offers transformative eventuality for enhancing cloud security. Still, this community presents significant challenges and limitations. This paper explores these challenges, including data sequestration enterprises, model interpretability issues, and the complexity of real- time trouble discovery. By analyzing current literature and empirical data, we identify critical areas where ML operations in cloud security are hindered. Our findings punctuate the need for robust encryption styles, transparent ML models, and effective anomaly discovery algorithms. Addressing these issues is pivotal for employing the full eventuality of ML in securing cloud surroundings.