Research on VM Selection and Placement in Fog Computing focuses on designing efficient strategies for selecting and deploying virtual machines (VMs) across distributed fog nodes to optimize performance, energy consumption, and resource utilization while maintaining Quality of Service (QoS) and Service Level Agreement (SLA) compliance. This area addresses challenges related to heterogeneous fog infrastructures, dynamic workloads, mobility, and limited computational resources at the edge. Key research directions include energy- and latency-aware VM placement algorithms, intelligent VM selection for load balancing, and predictive analytics for proactive placement decisions. Other emerging topics involve multi-objective optimization balancing cost, delay, and energy, fault-tolerant and migration-aware VM placement, and integration of container-based virtualization for lightweight deployment. Additionally, research on machine learning–enhanced VM management, edge–fog–cloud collaborative placement frameworks, and SLA-driven adaptive VM selection strategies represents significant avenues for advancing efficient, scalable, and intelligent fog computing systems.