  The application of machine learning (ML) in detecting, diagnosing, and
treating mental health disorders is garnering increasing attention.
Traditionally, research has focused on single modalities, such as text from
clinical notes, audio from speech samples, or video of interaction patterns.
Recently, multimodal ML, which combines information from multiple modalities,
has demonstrated significant promise in offering novel insights into human
behavior patterns and recognizing mental health symptoms and risk factors.
Despite its potential, multimodal ML in mental health remains an emerging
field, facing several complex challenges before practical applications can be
effectively developed. This survey provides a comprehensive overview of the
data availability and current state-of-the-art multimodal ML applications for
mental health. It discusses key challenges that must be addressed to advance
the field. The insights from this survey aim to deepen the understanding of the
potential and limitations of multimodal ML in mental health, guiding future
research and development in this evolving domain.
