Recent research in Energy-Efficient Virtual Machine (VM) Migration in Cloud Computing emphasizes optimizing power consumption while maintaining service quality and load balance across data centers. Modern studies introduce intelligent migration frameworks that minimize energy waste by dynamically relocating VMs from underloaded or overloaded hosts based on real-time resource utilization and thermal conditions. Advanced algorithms, including deep reinforcement learning, adaptive meta-heuristics, and hybrid optimization models, are employed to determine optimal VM selection and placement with minimal migration cost and downtime. These techniques aim to reduce total energy usage, enhance resource utilization, and prevent Service Level Agreement (SLA) violations. Furthermore, recent works integrate VM migration strategies with energy-aware consolidation, predictive analytics, and carbon-efficient scheduling to achieve sustainable cloud infrastructure operations.