Recent research in Energy Management in Cloud Computing focuses on developing intelligent strategies to reduce power consumption and improve sustainability in large-scale data centers without compromising performance or service quality. These studies introduce energy-aware algorithms for task scheduling, resource allocation, and virtual machine placement that dynamically adapt to workload fluctuations. Advanced methods leverage reinforcement learning, graph neural networks, and meta-heuristic optimization to predict energy demands, minimize idle resource usage, and balance computational loads efficiently. Hybrid approaches combining AI-driven forecasting with optimization models have shown significant energy savings and reduced operational costs. Moreover, energy management frameworks are now being extended to green cloud, edge, and IoT-integrated systems to enhance energy efficiency, scalability, and environmental sustainability.