Dynamic load balancing in distributed computing environments, especially with heterogeneous nodes, remains a significant challenge due to the fluctuating nature of workloads and resource availability. This paper presents a novel approach leveraging Deep Deterministic Policy Gradient (DDPG), a reinforcement learning algorithm, for optimal workload allocation in real-time systems. The system aims to minimize latency and maximize resource utilization by dynamically adapting to varying node metrics, including CPU usage, memory load, and latency. The DDPG model is trained on simulated state data, and real-time inference is performed through an API Gateway, enabling seamless integration with a five-node cluster. Results demonstrate that the proposed system outperforms traditional static and heuristic approaches in balancing workloads, optimizing resource utilization, and reducing latency. The approach is scalable, robust, and easily adaptable for edge and hybrid cloud architectures, providing a cost-effective solution for dynamic load balancing in distributed systems. This work bridges the gap between traditional cloud infrastructure and edge computing, ensuring efficient resource management in real-time systems.