The increasing prevalence of Man-in-the-Middle (MITM) attacks poses a significant threat to the security and privacy of data transmitted over public Wi-Fi networks. This study introduces a novel MITM attack detection mechanism that leverages the combined strengths of Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs). Existing machine learning approaches for detecting MITM attacks often struggle with limited accuracy and high false positive rates. To address these limitations, this research proposes a hybrid SVM-CNN model. CNNs excel at extracting intricate patterns from network traffic data, while SVMs are highly effective for classification tasks. By integrating these complementary strengths, the SVM-CNN model effectively identifies subtle anomalies indicative of MITM attacks. Simulation results demonstrate the superior performance of the SVM-CNN model compared to standalone SVM and CNN models. The SVM-CNN achieved a significantly higher detection accuracy (93%) with a significantly lower false positive rate (10%) compared to CNN (80% accuracy, 30% false positives) and SVM (75% accuracy, 45% false positives). These findings underscore the effectiveness of the proposed approach in enhancing the security and privacy of users on public Wi-Fi networks.