iarjetIARJETIARJET2708-5163https://doi.org/10.47310/iarjet.2026.v07i01.003Intelligent Network Slicing Optimization in 5G/6G Networks Using Deep Reinforcement LearningNetwork slicing is a key technology to support a range of services with diverse Quality of Service (QoS) requirements in 5G networks and the upcoming 6G networks. However, dynamic resource allocation to network slices presents a challenge due to dynamic traffic pattern and strict latency requirements. In this paper, we propose an intelligent network slicing optimization approach based on Deep Reinforcement Learning (DRL) to address these challenges. Our approach facilitates online decision-making by learning network resource allocation policies over time in response to changing traffic demands, priority and latency constraints. The DRL strategy is dynamic and allocates resources to maximise network performance while adhering to Service Level Agreement (SLA) requirements, as opposed to the static and rule-based resource allocation approaches. Our simulations have shown that the proposed model has improved resource efficiency, end-to-end delay and QoS satisfaction compared to traditional resource allocation strategies. Our findings demonstrate the advantages of using artificial intelligence for future network management, which enables scalable, adaptable and efficient 5G/6G networks.