The synergy between machine learning and cloud computing algorithms leads to enhanced results by increasing the efficiency of cloud computing, unlike the conventional approaches used in previous research. The allocation of virtual machines within cloud infrastructures continues to be a major challenge that has been extensively explored, yet it remains unsolved. Efficient techniques for virtual machine placement and migration are essential for optimal consolidation. In this paper, we make an exhaustive survey of the literature on VM migration using Machine Learning with resource utilization history which focuses on improving energy efficiency, VM migration counts, and service quality. Our analysis has targeted the objective, the algorithms used, the key contributions, results of diverse research. We also highlight different performance metrics, such as SLA Time Performance Active Host, SLA Performance Degradation due to Migration, Number of VM Migrations, Number of Host Shutdowns, Total Energy Consumption, Energy and SLA Violation.