The COVID-19 epidemic has caused stress, financial troubles, anxiety, and melancholy symptoms. As a result, mental health is a major worldwide concern. People with mental diseases sometimes do not receive the proper care and attention because of the lack of understanding about them. In contrast to bodily ailments like the flu or cough, mental health issues are frequently disregarded, making treatment less important. The earlier research in this field also outlines techniques that advance the improvement of a person’s mental condition by automating prediction tools for identifying physiological features. To address the concern, this study’s primary objective is to develop a model using ML techniques and various frameworks that can be able to predict mental health and be able to develop this using human voice. This kind of approach may lessen the burden on healthcare systems, improve treatment outcomes, and carry out tailored treatments by prioritizing those who are most in need of resources. For those with mental health concerns, the effect of this strategy involves early detection and one-on-one treatment, and improved outcomes will be provided. In this paper machine learning models demonstrated an increase in the degree of accuracy with 80% and precision of 79% which have been tested on different test scores.