Deep Q-Learning (DQL) has emerged as a promising method for enhancing Network Intrusion Detection Systems (NIDS) by enabling dynamic and adaptive detection of evolving network threats. This review examines the strengths, limitations, and potential enhancements of using DQL in NIDS. Even while DQL increases the accuracy of anomaly detection and manages massive amounts of network data, it has drawbacks such as slow convergence, high processing costs, and vulnerability to adversarial attacks. This study proposes improvements to overcome these problems, including efficient reward systems, hybrid architectures that combine DQL with other machine learning models, and continuous learning to adapt to changing threats. Recommendations for further research to enhance DQL’s efficacy in real-time intrusion detection are included in the study’s conclusion.