Recent research in VM Migration for Load Balancing in Cloud Computing focuses on developing intelligent and adaptive mechanisms that use live and dynamic migration of virtual machines to balance workloads across servers and data centers. These approaches monitor host utilization levels and migrate VMs from overloaded to underloaded hosts, improving performance, energy efficiency, and resource utilization while maintaining service-level agreements. Modern solutions incorporate machine learning, reinforcement learning, and multi-objective optimization techniques to predict workload variations, minimize migration overhead, and reduce downtime. VM migration-based load balancing is also being integrated into multi-cloud, edge, and fog environments to enhance scalability and responsiveness for latency-sensitive applications, ensuring seamless task execution and optimized system stability.