Energy-efficient VM selection and placement in fog computing is a crucial research area that focuses on determining which virtual machines (VMs) should be deployed, migrated, or replicated, and where they should be placed across distributed fog nodes to minimize energy consumption while ensuring Quality of Service (QoS) and low latency. Research papers in this domain explore strategies that consider heterogeneous node capabilities, dynamic workloads, network conditions, and energy constraints to optimize overall system efficiency. Studies highlight heuristic algorithms, metaheuristic approaches, optimization models, and machine learning techniques—including reinforcement learning and predictive analytics—for intelligent, context-aware, and energy-efficient VM selection and placement. Recent works also investigate multi-tier fog–edge–cloud architectures to enhance scalability, fault tolerance, and service continuity while reducing operational energy costs and migration overhead. Security- and privacy-aware frameworks are increasingly integrated to protect sensitive data during VM deployment and migration. Applications span smart healthcare, autonomous vehicles, industrial IoT, smart cities, and real-time multimedia services, where energy efficiency and performance are critical. Overall, research in energy-efficient VM selection and placement in fog computing enables adaptive, sustainable, and high-performance management of virtualized resources, ensuring reliable, low-latency, and energy-conscious fog computing environments.