aspg-18-3842-1792025787 20261015005627 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 17 1 Mohammad Khalid Department of Computer Engineering, Aliraqia University, 22 Sabaabkar, Adamia, Baghdad, 10053, Iraq Hassan Mohamed Muhi-Aldeen Department of Computer Engineering, Aliraqia University, 22 Sabaabkar, Adamia, Baghdad, 10053, Iraq Basma Rashid Mahdi Alhamdani Centre for Developing and Continuous Education, Aliraqia University, 22 Sabaabkar, Adamia, Baghdad, 10053, Iraq Due to the Internet's growing importance in our lives, Software-Defined Networking (SDN) networks have experienced high load traffic issues. Thus, network load has increased, lowering quality of service (Qos) performance. Modern networked systems depend on communication channels to transmit data between sources and destinations. High traffic loads exacerbate packet distribution inefficiencies, causing network congestion in specific channels, compromising these communication channels. Congestion delays packet delivery and generates significant packet loss, reducing network dependability and efficiency. Communication channels' improper packet allocation along accessible paths is the fundamental issue. Some paths are overcrowded during peak traffic, while others are underused. Bottlenecks slow packet transit and increase packet loss due to this imbalance. Current packet distribution techniques don't adapt effectively to dynamic traffic, resulting in poor network performance. Current traffic management solutions often rely on load balancing algorithms, but these methods may not adequately account for the dynamic and unpredictable nature of high-load traffic. This paper introduces Adaptive Load Balancing using Reinforcement Learning (ALBRL), which uses Q-learning and deep reinforcement learning to distribute traffic in real time in SDNs with high traffic loads. This model uses more network-specific indicators including packet loss ratio, latency, Jitter, and traffic pattern history to improve decision-making. ALBRL outperformed static routing and Q-learning with 15.34(ms) average delay, 2.11(ms) jitter, and 7.89% packet loss ratio. 2025 255 270 3842 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.170118 https://americaspg.com/journal/18/article/3842 https://americaspg.com/storage/81746692310.pdf