Research on Dynamic Resource Allocation in Cloud Computing focuses on designing adaptive strategies to allocate computational, storage, and network resources in real time based on changing workloads, user demands, and system conditions. This area aims to optimize performance, cost efficiency, energy consumption, and Quality of Service (QoS) in heterogeneous and large-scale cloud environments. Key research directions include predictive resource provisioning using machine learning, auto-scaling algorithms for elastic resource management, and SLA-aware allocation policies. Other emerging topics involve multi-objective optimization balancing performance, cost, and energy efficiency, container- and VM-level dynamic allocation, cloud–edge integrated resource management, and fault-tolerant adaptive frameworks. Additionally, research on reinforcement learning- and heuristic-based approaches for real-time resource adaptation, workload-aware scheduling, and energy-efficient dynamic allocation represents significant avenues for advancing intelligent and sustainable cloud computing infrastructures.