Recent research in VM Consolidation-based Load Balancing in Cloud Computing focuses on optimizing the placement and migration of virtual machines to achieve efficient resource utilization, energy savings, and balanced workload distribution across physical servers. By dynamically consolidating underutilized VMs and migrating them to less-loaded hosts, these techniques minimize energy consumption while maintaining service-level agreements and reducing operational costs. Modern studies employ predictive analytics, machine learning, and meta-heuristic algorithms to make intelligent consolidation and migration decisions in real time. This approach not only enhances performance and scalability but also mitigates hotspots and resource wastage in large-scale cloud data centers. Additionally, VM consolidation-based load balancing is increasingly being integrated with hybrid cloud and edge infrastructures to support sustainability and adaptive resource management.