This chapter provides an in-depth study of algorithms related to speech, audio, and video data to proactively detect mental anxiety and depression. The chapter emphasizes collecting the data from devices and providing know-how for merging textual and audio data in determining mental distress and risk levels. With an understanding of deep learning algorithms, this chapter delves into different metrics to assess mental health conditions and provide recommendations for treatment and support. This chapter introduces readers to diverse mental health parameters for measuring mental health conditions by illustrating with examples. This chapter also discusses leveraging synthetic data generation to predict mental distress accurately. By combining advanced processing techniques and deep learning, this chapter opens new avenues for prior data processing to enable accurate and sensitive detection of mental distress in today’s data-driven world. The readers at the same time are made aware of the best practices of selecting data and training the deep learning models to avoid misuse and inaccurate prediction.