The most common psychiatric disorder is Clinical Depression. More than fifteen percent of people undergo an incident of major depression sometime during their life. There is an increase in depressive disorders and other-regarding symptoms in past years, which leads to the importance of early detection of depression. Detection of depression can be automated by analyzing a person's behaviour and emotions. The depressive disorder influences auditory speech qualities, facial expressions, thinking, and some cardiovascular activities; hence depression can be recognized by analyzing such factors and also trying to find out which method is more suitable and reliable. Through preprocessing, features of different signals are extracted and further handled to develop a machine learning model. Neural Networks are a faster and efficient way to analyze depression. This survey briefs about different techniques and machine learning models for depression detection. © 2021 IEEE.