Load balancing in mobile cloud computing is a vital research area that focuses on distributing computational workloads efficiently across mobile devices, edge nodes, and cloud servers to optimize system performance, reduce latency, and improve energy efficiency. Research papers in this domain explore static, dynamic, and adaptive load balancing strategies that account for heterogeneous device capabilities, fluctuating workloads, network conditions, and Quality of Service (QoS) requirements. Studies highlight the use of heuristic algorithms, metaheuristic approaches, machine learning, and reinforcement learning to achieve intelligent and context-aware workload distribution. Recent works investigate multi-tier mobile cloud environments, incorporating edge, fog, and cloud layers, to ensure scalability, fault tolerance, and service continuity. Security- and privacy-aware load balancing techniques are also explored to protect sensitive data during task offloading and processing. Applications span smart healthcare, autonomous vehicles, industrial IoT, intelligent transportation systems, and mobile multimedia services. Overall, research in load balancing for mobile cloud computing enables adaptive, efficient, and reliable management of distributed workloads, ensuring optimized performance, resource utilization, and energy efficiency in dynamic mobile computing environments.