Energy-aware load balancing in cloud computing is a significant research area that focuses on distributing computational workloads across cloud resources efficiently while minimizing energy consumption and maintaining Quality of Service (QoS). Research papers in this domain explore static, dynamic, and adaptive load balancing strategies that consider heterogeneous server capabilities, varying workloads, network conditions, and energy constraints. Studies highlight heuristic algorithms, metaheuristic approaches, optimization models, and machine learning techniques—including reinforcement learning and predictive analytics—for intelligent, context-aware, and energy-efficient workload distribution. Recent works also investigate multi-tier cloud and edge–cloud architectures to improve scalability, fault tolerance, and service continuity while optimizing energy utilization across distributed data centers. Security- and privacy-aware frameworks are increasingly emphasized to ensure that energy-efficient load balancing does not compromise data confidentiality or system reliability. Applications span smart healthcare, autonomous vehicles, industrial IoT, smart cities, and latency-sensitive multimedia services, where both performance and energy efficiency are critical. Overall, research in energy-aware load balancing in cloud computing enables adaptive, efficient, and sustainable management of distributed workloads, ensuring high performance, energy savings, and reliable cloud services.