Recent research in Energy-aware Virtual Machine (VM) Selection and Placement in Cloud Computing focuses on optimizing data center energy consumption while maintaining system performance and minimizing Service Level Agreement (SLA) violations. Modern studies propose intelligent algorithms for selecting and placing VMs across physical hosts based on energy efficiency, workload prediction, and resource utilization. Techniques such as Quantum Particle Swarm Optimization, NSGA-III, Reinforcement Learning, and hybrid meta-heuristics are widely adopted to achieve balanced energy-performance trade-offs. These methods dynamically consolidate VMs, reduce the number of active servers, and minimize migration overhead. Additionally, energy-aware placement strategies are being extended to distributed and fog-cloud architectures to ensure sustainable resource management, carbon footprint reduction, and cost-effective cloud operations.