Wi-Fi networks are widely used in enterprise environments, providing seamless connectivity. However, Rogue Access Points (RAPs) pose a severe security risk by allowing attackers to intercept sensitive data and launch cyber-attacks. Conventional detection methods, such as manual auditing and rule-based Intrusion Detection Systems-[IDS] are often inadequate in large-scale environments. AI-driven solutions offer a promising alternative by leveraging real-time data analysis and pattern recognition techniques. In this paper we proposed an AI-based technique for auto-configure authorized APs, clients, detect and prevent rogue access points using machine learning (ML) approach, to secure the wireless network from unauthorized access. We proposed to use multiple ML algorithms for RAP detection such as Support-Vector-Machines [SVM] used for effectively classifies high dimensional data, Random Forest [RF] to Robust against over fitting and it is suitable for feature importance analysis and Artificial Neural Networks [ANNs] for identifying complex patterns in network behavior. By implementing our proposed framework we can secure WLAN from several cyber-attacks.