"Children with Down syndrome often face challenges in their emotional and cognitive development, impacting areas like social skills, memory, attention, and spatial awareness. While previous research has explored gamification and AI in clinical settings, there's a clear need for direct intervention through specific, cohesively designed assistive learning applications that address these unique cognitive and emotional needs. A Novel Gamification-Based Approach This research proposes a novel gamification-based multimodal approach leveraging modern deep-learning techniques to bridge this gap. The goal is to create a single, comprehensive assistive application that integrates three crucial capabilities: Interactive Storytelling Module: This module allows children to engage with various awareness scenarios, receiving dynamic feedback generated by OpenAI's GPT-4 model. Their progress is systematically documented, providing valuable insights for educators. Facial Emotion Recognition System: This system uses a Fractal Neural Network enhanced by a secondary HDC classifier and optimized through a Genetic Algorithm. To ensure transparency, Explainable AI (XAI), specifically Grad-CAM, is incorporated to visualize the system's decision-making process. Speech Emotion Recognition System: Built on a hybrid feature set, this system combines traditional audio features (such as Chroma, Mel Spectrogram, and Spectroid) with advanced descriptors like Topological Data Analysis, Spectral Flux, Glottal Dynamics, and Teo with auto envelope features. The model design utilizes Ordinary Differential Equations (ODE), Bidirectional Gated Recurrent Units (BiGRU), and Attention methods to enhance emotional categorization accuracy. Accuracy and Future Directions The proposed study demonstrated a multimodal accuracy of 81% for emotion recognition and 51% for spoken emotion recognition. It's important to note that the research acknowledges limitations in generalizing datasets for each modality, which influenced the achieved accuracy levels. This opens up a promising avenue for open research to further enhance the accuracy and robustness of these assistive learning applications for children with Down syndrome."