AI-Powered Mental Health Diagnostics Using Multimodal Data: A Deep Learning-Based Approach

Abstract In recent years, the growing prevalence of mental health disorders has highlighted the urgent need for efficient, scalable, and accurate diagnostic tools. Traditional methods often rely on self-reporting and clinical interviews, which can be limited by subjectivity and accessibility. This research presents an AI-powered system for mental health diagnostics using multimodal data—integrating natural language (text), audio (speech tone and pitch), and visual (facial expression and micro- movements) inputs. By combining these modalities, the system captures a more comprehensive understanding of emotional and psychological states. Deep learn- ing models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based architectures, are employed for feature extraction and classification. The model is trained and validated on benchmark datasets such as DAIC-WOZ and AVEC. The results show enhanced diagnostic performance over unimodal systems, offering potential as a non-invasive, real- time, and accessible tool for early mental health screening and support. This work contributes toward ethical, AI-assisted mental healthcare. Keywords: Artificial Intelligence, Mental Health, Multimodal Data, Deep Learning, Emotion Recognition, NLP, Speech Analysis, Facial Expression