Research on VM Migration for Load Balancing in Fog Computing focuses on developing efficient mechanisms to dynamically migrate virtual machines (VMs) across fog nodes to achieve balanced resource utilization, reduced latency, and improved Quality of Service (QoS) in distributed fog environments. This area addresses challenges such as limited computational capacity, network variability, node mobility, and energy constraints inherent in fog infrastructures. Key research directions include designing lightweight and energy-efficient VM migration algorithms, predictive and workload-aware migration strategies, and latency-optimized migration decisions to minimize service disruption. Other emerging topics involve multi-objective optimization balancing load, energy, and cost; live migration techniques for real-time applications; and hybrid VM–container migration for improved flexibility. Additionally, research on fault-tolerant and SLA-compliant migration frameworks, machine learning–driven migration prediction, and fog–cloud collaborative migration strategies represents significant avenues for advancing adaptive, efficient, and reliable load balancing in fog computing systems.