Recent research in Heuristic-based Load Balancing in Cloud Computing explores the use of nature-inspired and optimization-driven algorithms to efficiently distribute workloads, enhance scalability, and minimize execution time in dynamic cloud environments. Techniques such as Particle Swarm Optimization, Genetic Algorithms, Ant Colony Optimization, Grey Wolf Optimization, and hybrid approaches combining multiple heuristics are increasingly applied to achieve balanced task scheduling and improved resource utilization. These algorithms dynamically adapt to workload fluctuations, reduce energy consumption, and optimize performance metrics like makespan and throughput. By leveraging intelligent heuristic mechanisms, recent studies enable effective load balancing across heterogeneous and large-scale cloud infrastructures, supporting the evolving needs of edge, fog, and IoMT-integrated computing systems.