Abstract
Mental health disorders constitute a significant global health challenge, affecting nearly one billion individuals and imposing substantial societal and economic burdens. Current diagnostic and therapeutic approaches are hindered by subjectivity, resource limitations, and insufficient personalization. This research investigates the application of machine learning (ML) techniques to advance the prediction, diagnosis, and treatment of mental health conditions. Leveraging multimodal datasets—including neuroimaging scans, wearable device metrics, and natural language data—this study evaluates the efficacy of advanced ML models such as Random Forests, Convolutional Neural Networks, and transformer-based architectures. Results demonstrate that multimodal ML approaches enhance diagnostic accuracy and enable significant improvements in personalized treatment strategies. Additionally, critical ethical considerations, including data privacy, algorithmic bias, and model interpretability, are addressed to propose a robust framework for the responsible integration of ML in mental healthcare.