Facial recognition technology for child detection is essential in applications such as security and missing child identification. This study enhances feature extraction and machine learning techniques for child face detection. Unlike traditional methods that train a single network, the proposed model comprises two specialized sub-modules: one trained for frontal faces and the other for tilted faces. The outputs of these modules are combined using logical OR operation, improving the system’s robustness to variations in facial orientation. Experimental results show that the proposed dual-module machine learning achieves over 91% accuracy, significantly outperforming the conventional approach, which attains only 86%. These findings underscore the effectiveness of multi-module training in enhancing child face recognition accuracy, making it a promising solution for real-world applications.