This paper is presented to explain the intersection of cloud computing (CC) and machine learning (ML), focusing on their synergies, challenges and solutions. It shows the changes in the Internet service area led by cloud computing (CC) and the economic impact of data collection and analysis. The document specifically addresses security issues in distributed models and introduces the concept of edge computing as a version of cloud computing (CC) for time-sensitive data. In this paper, we are discussing about data encryption, distribution of rights, and transfer of data responsibility from service providers to end users. The document breaks down Cloud computing into service and delivery models, addressing security issues related to integrity, availability, and identity threats. It offers machine learning (ML) algorithms as a solution to security and data quality management. With the help of this paper, we are highlights the challenges of integrating Cloud Computing (CC) and machine learning (ML), including data exchange latency, scalability optimization, resource management, data security, model deployment, and monitoring. A resource plan is provided to train organizations in the use of Cloud Computing (CC) and machine learning (ML). The summary ends by highlighting the evolution of Cloud Computing (CC) and machine learning (ML) integration to shape the future of computing and analytics and make organizations more competitive in the digital age.