Recent research in Virtual Machine Selection and Placement in Cloud Computing emphasizes intelligent and energy-efficient strategies to determine which VMs should be migrated and where they should be placed to optimize performance, cost, and resource utilization. These studies focus on reducing energy consumption, network latency, and service-level agreement violations by leveraging machine learning, reinforcement learning, and evolutionary algorithms for predictive and adaptive decision-making. Modern approaches use multi-objective optimization techniques to balance trade-offs between power efficiency, workload distribution, and system reliability. VM selection and placement have also been extended to multi-cloud and edge environments, where dynamic policies enable real-time adjustments for scalability, improved QoS, and enhanced resource management across distributed infrastructures.