Recent research in Energy-Efficient Load Balancing Techniques in Cloud Computing focuses on minimizing energy consumption while maintaining balanced resource utilization and high performance across data centers. These studies develop intelligent algorithms that dynamically distribute workloads among virtual machines based on energy profiles, utilization levels, and performance metrics. Modern approaches employ artificial intelligence, multi-objective optimization, and heuristic or meta-heuristic algorithms such as Genetic Algorithm, Particle Swarm Optimization, and Fuzzy Logic to optimize both load and power usage simultaneously. Energy-efficient load balancing frameworks also integrate cooling and thermal management strategies, ensuring reduced carbon footprints and operational costs. Additionally, emerging works extend these techniques to hybrid cloud, fog, and edge computing environments, enabling sustainable and scalable energy management for diverse and latency-sensitive applications.