Research on Dynamic Resource Allocation in Cloud Computing focuses on developing intelligent approaches to optimize system efficiency, resource utilization, and cost-effectiveness in real-time cloud environments. Recent studies highlight frameworks that integrate machine learning and reinforcement learning techniques, such as Q-learning, LSTM-based demand prediction, and DQN for dynamic scheduling, to make adaptive allocation decisions. Hybrid strategies leveraging optimization algorithms like Whale Optimization further enhance resource management, while AI-driven frameworks for microservices in hybrid clouds demonstrate significant improvements in efficiency and cost reduction. These advancements collectively emphasize the importance of adaptive, intelligent algorithms in ensuring scalable and responsive resource allocation in modern cloud computing systems.