Wireless communication technologies, particularly those based on IEEE 802.11, have significantly improved connectivity but remain highly vulnerable to Denial-of-Service (DoS) attacks. These attacks, which exploit protocol weaknesses and resource limitations, can severely disrupt network availability, particularly in mission-critical applications such as healthcare, financial services, and industrial control systems. In this research, we investigate various DoS attack techniques targeting IEEE 802.11 networks, including deauthentication flooding, disassociation attacks, authentication request flooding (AuthRF), association request flooding (AssRF), and cascading DoS attacks. To mitigate these threats, we analyse IEEE 802.11w, which provides management frame protection (MFP), and evaluate its effectiveness under different attack scenarios. The model integrates supervised learning for attack classification, unsupervised learning for detecting novel threats, and reinforcement learning for adaptive mitigation strategies. Additionally, the system incorporates IEEE 802.11w security enhancements and anomaly-based behaviour analysis to strengthen network resilience. This study provides a comprehensive review of existing DoS attack mechanisms, explores recent mitigation techniques, and introduces an advanced IDS framework to safeguard IEEE 802.11 networks against sophisticated cyber threats. Finally, the analysis is organized through a survey that evaluates the articles based on publication year, research techniques, performance metrics, toolset and utilized database.