Recent research in Dynamic Load Balancing Techniques in Cloud Computing focuses on designing intelligent and adaptive mechanisms that can efficiently manage fluctuating workloads across heterogeneous virtualized environments. These approaches continuously monitor system states and dynamically reallocate tasks to optimize resource utilization, reduce response time, and maintain service quality. Advanced models employ reinforcement learning, deep learning, and hybrid optimization algorithms to make real-time decisions for task migration and virtual machine allocation. Unlike static methods, dynamic load balancing adjusts automatically to unpredictable workload variations, ensuring fault tolerance and energy efficiency. Recent studies have also extended these techniques to hybrid cloud–edge–fog systems, enhancing scalability and performance for latency-sensitive and data-intensive applications.