Load balancing in edge computing is a vital research area that addresses the efficient distribution of workloads across heterogeneous and resource-constrained edge, fog, and cloud nodes to ensure system reliability, low latency, and optimal resource utilization. Research papers in this domain investigate static and dynamic load balancing techniques designed to handle fluctuating workloads, user mobility, and diverse application requirements such as real-time analytics, autonomous vehicles, smart healthcare, and industrial IoT. Studies highlight heuristic algorithms, game-theoretic models, and optimization-based methods for task distribution, while recent works focus on machine learning and deep reinforcement learning approaches for adaptive and predictive load balancing. Security-aware and energy-efficient load balancing frameworks are also gaining attention to mitigate risks of overload attacks, minimize power consumption, and extend device lifetime. Furthermore, collaborative load balancing strategies across multi-access edge computing (MEC), fog, and cloud infrastructures are being explored to enhance scalability, resilience, and service continuity. Overall, load balancing research in edge computing plays a critical role in improving system performance, ensuring fairness, and enabling reliable delivery of latency-sensitive applications in dynamic and distributed environments.