Research on Energy Efficient VM Migration in Cloud Computing focuses on developing strategies to transfer virtual machines (VMs) between physical servers while minimizing energy consumption, reducing migration overhead, and maintaining Quality of Service (QoS) and Service Level Agreement (SLA) compliance. This area addresses challenges related to dynamic workloads, heterogeneous resources, and large-scale cloud infrastructures. Key research directions include designing energy-aware VM selection and placement algorithms, live VM migration techniques that optimize power usage, and predictive workload modeling for proactive migration decisions. Other emerging topics involve multi-objective optimization balancing energy, cost, and performance, integration of Dynamic Voltage and Frequency Scaling (DVFS) with migration strategies, cloud–edge hybrid migration for latency-sensitive applications, and heuristic or metaheuristic approaches (e.g., genetic algorithms, particle swarm optimization) for energy-efficient migration planning. Additionally, research on fault-tolerant, SLA-compliant, and machine learning-enhanced VM migration frameworks represents significant avenues for advancing sustainable and efficient cloud data center operations.