Recent research in Load Balancing in Cloud Computing focuses on developing intelligent and adaptive strategies to efficiently distribute workloads across virtual machines and servers, ensuring high resource utilization, minimal response time, and improved energy efficiency. Modern approaches leverage techniques such as reinforcement learning, metaheuristics, fuzzy logic, and multi-criteria decision-making to handle dynamic and heterogeneous cloud environments. These methods enable real-time workload redistribution and prevent performance bottlenecks caused by uneven task loads. By integrating AI-driven decision models and predictive analytics, recent studies aim to enhance scalability, reduce energy consumption, and maintain service-level agreements. Such adaptive load-balancing mechanisms are increasingly applied to hybrid cloud–fog–edge ecosystems, supporting latency-sensitive applications like IoMT and federated systems.