The ever-growing requirement of on-demand, scalable and efficient cloud computing infrastructure has raised an important issue of load dissemination in distributed clouds. The traditional load balancing algorithms including round-robin, weighted random are simple and widely deployed. Nevertheless, they frequently cannot deal with the dynamic, heterogeneous, and non-stationary workloads of current cloud systems. In this paper, we propose an AI-powered load balancing model that uses Reinforcement Learning (RL) to determine the distribution of computing resources on-the-fly, in accordance with the performance of the system at each given time. My model is adaptive and accurately tracks cloud states (e.g., CPU utilization, task queues, network latency) to dynamically adjust the allocation policy based on current observations to achieve better overall system performance. In contrast to static methods, an emerging RL-based scheme can provide run time adaptability and enables the system to proactively react to workload changes and unexpected resource requests. The framework is scalable and can be deployed in wide and distributed cloud infrastructures. The performance of the model was verified experimentally in simulation with a cloud environment, and benchmarked with traditional methods. The results indicate that the proposed AI-powered load balancer is able to increase resource utilization, reduce response time, and enhance the system throughput. These results highlight the promise of harnessing AI in cloud management systems for achieving smarter, greener, and more resilient computing infrastructures. This work paves the way for future studies about the interaction between sophisticated RL algorithms and hybrid AI approaches in order to achieve better performance in cloud service provision.