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Man in the middle (MITM) attacks can dramatically compromise the security of Wi-Fi network where an attacker eavesdrops and intercepts the communication medium over the wireless communication networks. This kind of attack aims to steal sensitive data such as credit card details, login accounts, and other important financial transactions. Even though that many detection techniques have been proposed to mitigate MITM attacks, however, this attack still occurs and causes tremendous damages. In this study, we propose a set of machine learning techniques to detect and identify MITM attacks on a wireless communication network. In addition, we evaluate and validate our approach based on the performance metrics, and compare the performance results with other machine learning techniques.