Artificial Intelligence and Machine Learning Role in Enhancing Wireless Network Security

The growth of wireless networks has transformed the contemporary communication infrastructure, but wireless transmission is a broadcast and poses severe security risks to confidential activities in the health, finance, and government industries. Conventional signature-based security systems are not quite sufficient to handle the dynamic, advanced, and changing environment of cyber attacks against wireless ecosystems. This is a wide-ranging systematic review that explores the use of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing wireless network security in Wi-Fi, Internet of Things (IoT), Wireless Sensor Networks (WSN), and newer 5G/6G networks through the study of peer-reviewed literature published between 2018 and 2025. The paper follows the history of wireless security protocol development, starting with a fundamentally flawed protocol known as Wired Equivalent Privacy (WEP), through WPA/WPA2, and finally the current standard of wireless security protocol development, WPA3. It further taxonomically classifies the current attack vectors including eavesdropping, man-in-the-middle exploitation, rogue access points, evil twin impersonation, session hijacking, SSID confusion, and protocol downgrade attacks. We show how smart AI/ML designs, such as anomaly detection, supervised learning, unsupervised clustering, reinforcement learning, and graph neural networks, can allow real-time threat detection, predictive analytics, as well as autonomous response capabilities that are beyond traditional rule-based designs. Nevertheless, despite these positive developments, there are still serious challenges: the computational complexity of these systems prevents their application on IoT systems with resource constraints, adversarial examples are susceptible, high quality labeled data is required, scaling to ultra-dense networks is not achievable, and deep learning models lack interpretability. This review offers the unique analysis of wireless focus, indicating the knowledge gaps in federated learning in heterogeneous conditions, clarifiable AI infrastructures to manage regulatory standards, quantum-resilient algorithms in post-quantum security, and light-weight model designs in edge computing. The future research focus areas are integration of zero-trust, privacy-sensitive methods, and human-AI co-designing approaches to create robust wireless security infrastructure with the capacity to withstand future cyber offenses.