Because cloud workloads and service demand shift continuously, cloud platforms need resource-allocation mechanisms that adjust on the fly to keep quality of service (QoS) high, contain operating expenses, and avoid the common failure mode in which some servers sit idle while others are pushed past capacity. What makes this hard is that cloud environments are dynamic and their states keep changing, so allocation decisions cannot rely on fixed rules. Conventional approaches typically require expert tuning or repeated manual iteration, which makes them costly and difficult to adapt. A central difficulty is therefore assigning resources efficiently enough to satisfy many concurrent users and applications with competing requirements. Reinforcement learning (RL), through its exploration-exploitation mechanism, offers a way to keep adapting as conditions evolve. This paper's central argument is that dynamic-workload resource allocation in cloud settings can be handled effectively with RL-based methods, which let systems learn from ongoing changes, adjust performance in real time, and cut costs accordingly. The review further finds that hybrid schemes pairing meta-heuristic search with reinforcement learning surpass conventional RL algorithms once tested on real-world data.