With the growing prevalence of mental health disorders across the globe, the application of Artificial Intelligence (AI) and Machine Learning (ML) has gained significant attention for early detection, prevention, and intervention. This study explores various AI-based models used for mental health self-assessment, including traditional machine learning techniques such as Support Vector Machines (SVM), Logistic Regression, and Random Forest, as well as advanced deep learning approaches. Furthermore, the paper reviews commonly used datasets and highlights the role of Natural Language Processing (NLP) tools in analyzing user-generated data for identifying mental health patterns. Ethical concerns such as data privacy, bias, and transparency are also discussed, along with the feasibility of deploying these solutions through web-based platforms. The objective of this study is to summarize recent advancements and identify existing research gaps, thereby supporting the development of scalable, accessible, and ethically responsible AI-driven mental health systems.
Bhute et al. (Mon,) studied this question.