Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3844 2019 2019 Minister Office of Higher Education and Scientific Research, Iraq Oday Oday College of Computer Science and Information Technology, Wasit University, Iraq Ghaith Ali Hussein Ministry of Education, Wasit Education Directorate. Iraq Bushra Majeed Muter Ministry of Education, Wasit Education Directorate. Iraq; Computer Department, College of Education for Pure Sciences, Wasit University, Iraq Oday Ali Hassen Software-Defined Networks (SDNs) offer unparalleled network control flexibility, yet efficient load balancing is still challenging in dynamic environments. DeepBalance is a novel framework presented in this paper, which deploys dynamic load balancing in SDNs using Deep Reinforcement Learning (DRL). Our solution employs a Deep Q-Network (DQN) agent, which learns the optimal routing policies by monitoring network states and being rewarded based on load distribution. DeepBalance continuously tracks link utilization and intelligently reshifts traffic to alleviate congestion and achieve maximal throughput. We employ a comprehensive simulation environment, which emulates actual network conditions and traffic patterns. Experimental results demonstrate that DeepBalance significantly outperforms traditional load balancing techniques, lowering link utilisation variance by 37% and total throughput by 28% over shortest-path routing. The infrastructure adapts with changing traffic patterns automatically without the necessity of manual reconfiguration, thus naturally circumventing hotspots by making forward-looking path decisions. Additionally, our visualizations illustrate how the DRL agent learns over time to distribute network load more evenly across alternative paths. DeepBalance is a strong candidate for autonomous network optimization in future SDN deployments. 2025 2025 279 290 10.54216/JISIoT.170120 https://www.americaspg.com/articleinfo/18/show/3844