Recognizing emotions in children presents a unique challenge, as their emotional expressions are often subtle, variable, and influenced by developmental factors. Traditional recognition methods frequently fall short in capturing these complexities. In response, this study introduces a comprehensive deep learning-based approach designed specifically for classifying children’s emotions. The methodology includes systematic data collection, advanced feature extraction, and robust classification processes. A curated dataset was developed featuring three primary emotional states, happiness, sadness, and anger. Deep neural networks were employed to automatically identify and learn meaningful patterns from the data, allowing for the extraction of high-level features essential for accurate emotion recognition. To ensure practical applicability, the trained model was integrated into a web-based interface, enabling real-time emotion analysis through API interactions with user-submitted images. Evaluation results indicate that the model achieves reliable predictive performance. This research not only contributes to improving the accuracy of automated emotion detection in children but also provides a foundation for future innovations in educational technology, mental health monitoring, and affective computing tailored to young users. Experiments conducted on real-world datasets demonstrate that the proposed system outperforms conventional approaches in accuracy with 95%.