Pathiraja G.P.KNavarathne N.M.D.CNavodya D.P.D.D.Dharmapriya R.A.P.Vidanaralage, A.JVidanaralage, A.J2026-08-192026-06-26979-833156170-3https://rda.sliit.lk/handle/123456789/5249Children often struggle to express their emotions verbally, making early detection of mental health issues challenging and limiting timely intervention. This research proposes an AI-powered mobile application designed to support children's mental well-being through multimodal emotional analysis and personalized interventions. The significance of this study lies in addressing the lack of accessible, child-friendly, and culturally relevant digital mental health solutions, particularly for Sinhala-speaking users, while reducing dependence on subjective expert interpretation. The proposed system integrates four main components, including a culturally grounded therapeutic story generator using a GRU-based deep learning model, an emotion analysis module for children's drawings utilizing DenseNet121 and YOLOv8 with colour and spatial feature extraction, an emotion-aware music recommendation and adaptive puzzle game powered by EfficientNet-based facial emotion recognition, and a Sinhala voice-first mood prediction system using fine-tuned XLM-RoBERTa.Experimental results demonstrate strong performance, including improved contextual coherence and adaptive therapeutic storytelling capabilities, 79% accuracy in drawing-based emotion classification, 87% accuracy in emotion-aware music recommendation, and 96% accuracy in Sinhala text-based mood prediction, along with improved user engagement through personalized storytelling and adaptive feedback mechanisms. The system further enhances interpretability by generating parent-friendly emotional reports using large language models. In conclusion, this research contributes a comprehensive, multimodal, and culturally adaptive AI solution for supporting children's mental health, enabling early emotional detection, improved communication, and scalable intervention through an engaging mobile platform.enAI based storytellingchild mental healthdrawing-based emotion detectionemotion recognitionfacial emotion recognitionmobile health applicationmusic recommendation systemConference PaperDOI: 10.1109/I2CACIS69435.2026.11600171