Load balancing and migration in fog computing is a vital research area that focuses on distributing workloads efficiently across fog nodes and dynamically migrating tasks to optimize system performance, reduce latency, and enhance resource utilization. Research papers in this domain explore static, dynamic, and adaptive load balancing strategies that consider heterogeneous node capabilities, fluctuating workloads, network conditions, and Quality of Service (QoS) requirements. Studies highlight the use of heuristic algorithms, metaheuristic approaches, optimization models, and machine learning techniques—including reinforcement learning—for intelligent, context-aware load balancing and task migration. Recent works investigate multi-tier fog–edge–cloud architectures to improve scalability, fault tolerance, and service continuity, ensuring real-time processing for latency-sensitive applications. Security- and privacy-aware load balancing and migration frameworks are increasingly emphasized to protect sensitive data during task relocation and execution. Applications span smart healthcare, autonomous vehicles, industrial IoT, smart cities, and real-time multimedia services. Overall, research in load balancing and migration in fog computing enables adaptive, efficient, and reliable management of distributed workloads, ensuring high performance, resilience, and energy-efficient operation in next-generation fog computing environments.