Recent research in Resource Allocation Optimization in Cloud Computing focuses on developing intelligent and adaptive mechanisms to enhance performance, cost efficiency, and energy utilization across dynamic cloud environments. Advanced models integrate machine learning, deep reinforcement learning, and hybrid meta-heuristic algorithms to predict workloads, allocate resources efficiently, and maintain Quality of Service (QoS). Studies also explore multi-objective optimization strategies that balance latency, throughput, and energy consumption in virtualized and serverless infrastructures. The latest approaches emphasize real-time decision-making, fairness among tenants, and scalability, enabling optimized resource distribution in heterogeneous and multi-cloud ecosystems.