This research shows a complete machine learning framework that can help advance mental health research. It includes five different algorithms: improved supervised learning, natural language processing (NLP), deep learning for multimodal fusion, ensemble learning, and genomic data analysis. The framework demonstrates its versatility by seamlessly integrating diverse data modalities, including clinical text, neuroimaging, and genomic data. An ablation study highlights the synergistic strength of the entire framework, emphasizing the significant impact of each algorithm on overall performance. Performance evaluations, presented in two tables and visualized through bar charts, line charts, stacked bars, and area charts, consistently affirm the proposed method’s superiority over existing techniques. The collective learning component emerges as a key contributor to enhanced robustness, showcasing the importance of model aggregation in mental health prediction. The visual representations provide an intuitive understanding of the framework’s strengths, highlighting accuracy, sensitivity, specificity, and precision trends. The presented algorithms collectively form an integrated pipeline, offering a holistic approach to mental health diagnostics and prediction. As mental health research progresses, this framework stands as a promising tool for researchers, clinicians, and practitioners seeking accurate, interpretable, and comprehensive insights into mental health conditions. Future directions involve refining algorithms, incorporating real-world data, and conducting clinical validations to solidify the framework’s applicability in diverse healthcare settings.