In this paper, we propose a novel approach to load balancing in cloud computing environments using the Deep Deterministic Policy Gradient (DDPG) algorithm, a model-free, off-policy reinforcement learning method. The growing complexity and dynamic nature of cloud services demand efficient and adaptive load balancing techniques to optimize resource utilization and minimize response time. Traditional load balancing methods often fall short in addressing these requirements due to their static policies and inability to adapt to changing conditions. We address these challenges by implementing a DDPG-based framework that dynamically adjusts its strategies according to the state of the system. The DDPG algorithm, which combines the strengths of Deep Learning and Reinforcement Learning, enables our model to continuously learn and refine its policy based on the reward feedback from the environment. This allows for a more flexible and efficient distribution of computing resources in real time. To evaluate the effectiveness of our approach, we conduct extensive simulations in a simulated cloud computing environment. Our results demonstrate significant improvements in load distribution, reduced latency, and enhanced overall system performance compared to conventional load balancing strategies. Furthermore, our approach exhibits robust adaptability to various operational scenarios, indicating its potential for practical deployment in real-world cloud systems. This study not only advances the application of deep reinforcement learning in cloud computing but also provides a scalable solution to the challenges posed by the ever-increasing demand for cloud services. The implications of our findings are discussed in terms of both technical performance and strategic deployment, paving the way for future research in this promising area.