The popularity and use of Cloud computing is growing rapidly. Many companies invest in the industry to use it for
themselves or to provide it as a service to others. One of the consequences of cloud development is the emergence of various
security issues in both the industry and consumer. One of the ways to protect the Cloud is to use Machine Learning (ML). ML
techniques have been used in a variety of ways to prevent or detect attacks and security gaps in the Clouds. In this paper, we
provide comparative analysis of cloud computing for ML, Azure, GCP, AWS methods and methods. We analyzed 63 relevant
studies and the SLR results were divided into three main research categories: (i) different types of cloud security threats, (ii) ML
strategies used, and (iii) operational outcomes.
