Recent research in Adaptive Resource Allocation in Cloud Computing emphasizes the use of artificial intelligence and optimization strategies to dynamically manage resources and balance performance, cost, and energy efficiency under changing workloads. Advanced approaches such as deep reinforcement learning, hybrid metaheuristics, and predictive models like LSTM and Bayesian forecasting are applied to enable intelligent and context-aware resource scheduling across multi-cloud, serverless, and edge environments. These adaptive mechanisms enhance scalability, minimize latency, and ensure SLA compliance while efficiently utilizing computational resources. The integration of forecasting and adaptive decision-making also supports emerging applications in IoMT, Edge AI, and federated learning-driven cloud systems, providing a foundation for more responsive and self-optimizing infrastructures.