Dynamic load balancing in fog computing is a crucial research area that focuses on the real-time distribution of computational tasks across geographically distributed fog nodes to optimize resource utilization, reduce latency, improve Quality of Service (QoS), and ensure energy efficiency. Research papers in this domain explore adaptive and context-aware load balancing strategies that respond to fluctuating workloads, heterogeneous node capabilities, network conditions, and mobility of edge devices. Studies highlight heuristic algorithms, metaheuristic approaches, optimization models, and machine learning techniques—including reinforcement learning and deep learning—for intelligent and autonomous task allocation. Recent works also investigate multi-tier fog–edge–cloud architectures, incorporating task migration, service replication, and workload prediction to enhance scalability, fault tolerance, and service continuity. Security- and privacy-aware dynamic load balancing frameworks are increasingly emphasized to protect sensitive data while maintaining efficient and reliable task execution. Applications include smart healthcare, autonomous vehicles, industrial IoT, smart cities, and latency-sensitive multimedia services. Overall, research in dynamic load balancing in fog computing enables adaptive, efficient, and resilient management of distributed workloads, ensuring optimal performance, resource utilization, and reliability in next-generation fog computing ecosystems.