Recent research in Energy-aware Resource Scaling in Cloud Computing focuses on dynamically adjusting computing resources to minimize energy consumption while maintaining performance and Quality of Service (QoS). Modern approaches employ predictive and adaptive scaling mechanisms that monitor workload variations and automatically scale resources up or down based on real-time demand. Techniques such as machine learning-based forecasting, reinforcement learning, and heuristic optimization are widely used to achieve efficient energy-performance trade-offs. These methods combine horizontal and vertical scaling strategies with power-aware scheduling to reduce idle energy usage in cloud servers. Additionally, energy-aware scaling frameworks are being extended to multi-cloud, edge, and serverless environments, ensuring sustainable resource utilization and reduced operational costs across heterogeneous cloud infrastructures.