VM selection and placement in fog computing is a critical 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 optimize resource utilization, reduce latency, and maintain Quality of Service (QoS). Research papers in this domain explore strategies that consider heterogeneous fog node capabilities, dynamic workloads, energy constraints, network conditions, and application-specific requirements. Studies highlight heuristic algorithms, metaheuristic approaches, optimization models, and machine learning techniques—including reinforcement learning and predictive analytics—for intelligent and context-aware VM selection and placement decisions. Recent works also investigate multi-tier fog–edge–cloud architectures to improve scalability, fault tolerance, and service continuity while minimizing deployment and migration overheads. Security- and privacy-aware VM selection and placement frameworks are increasingly emphasized to protect sensitive data and ensure reliable execution in multi-tenant environments. Applications span smart healthcare, autonomous vehicles, industrial IoT, smart cities, and real-time multimedia services. Overall, research in VM selection and placement in fog computing enables adaptive, efficient, and resilient management of virtualized resources, ensuring optimal performance, energy efficiency, and reliability in next-generation fog computing ecosystems.