The Evil Twin Attack (ETA), which uses rogue Wi-Fi access points to trick users into connecting and expose their private information, is a serious and advanced threat to wireless network security. Data theft, illegal access, and even more extensive cyberattacks against linked systems can result from this attack. Traditional detection techniques are unable to protect against this misleading attack because it takes advantage of the trust that users invest in networks that appear familiar. Improving detection methods is essential given the increase in wireless connectivity and the simplicity with which ETAs may be implemented. By examining traffic patterns that can point to unusual activity, this dissertation investigates the potential of machine learning (ML) in ETA detection. The main objective of this study is to assess how well several machine learning techniques, including neural networks, Support Vector Machines (SVM), and decision trees, detect rogue access points in real time. The study evaluates the capacity of each model to detect ETAs by training these algorithms on network traffic data and calculating performance in terms of accuracy, computational efficiency, and false positive rates. By learning to identify intricate patterns and anomalies in network data, these models enable the prompt detection of questionable access points that could otherwise go unnoticed. Based on extensive testing, this study shows that ML-based detection models provide a reliable and scalable ETA detection solution that strikes a balance between high accuracy and controllable processing requirements. This study makes a significant contribution to the field of network security, since it offers a versatile and adaptable remedy for one of the more difficult cyberthreats that wireless networks are currently confronting. This dissertation emphasizes the value of machine learning approaches in defending against new cyberthreats by showing how they may be applied in practical situations. Machine learning is a useful weapon in the continuous battle against ETAs thanks to its capacity to adjust to changing attack methods. This study highlights how important proactive, intelligent systems are to protecting user data and privacy in the connected world of today.