The IoT is a network of networked devices that collect and deliver data. These devices are equipped with embedded software, sensors, electronics, and network connectivity. Because companies are unable to handle the massive volume of data created by IoT, AI, and cloud computing are being merged. Cloud computing facilitates device interoperability by providing on-demand access to programs, processing power, database storage, and IT services. Cloud installations are classified into four types: community, private, hybrid, and public. The combination of AI methods such as deep learning and ML with cloud computing has resulted in greater capabilities, intelligence, and scalability. However, as the IoT grows in scope, security issues such as integrity, availability, and confidentiality become increasingly important. To address these issues, cloud service providers (CSPs) are increasingly relying on ML security technology to detect and fix security flaws. This essay discusses the multiple potentials afforded by this fast-expanding topic and provides a comprehensive evaluation of ML in cloud security. In cloud security, Random Forest and Decision Tree algorithms are widely used to detect anomalies, categorize network traffic, and detect intrusions. Although K-Means clustering is a computationally efficient approach, it may not be suitable for non-numerical variables or categorical data. This paper looks at recent advances in ML-based cloud security solutions, diving into the characteristics of various algorithms, their strengths and limits, and emphasizing the promise of this developing discipline in improving cloud security.