The explosion in the number of digital gadgets and the increasing integration of technology into daily life have led to a significant rise in screen time usage across various age groups. Also, the pandemic has moved us indoors leading every work to be done online. The screen time including the smart phones, Laptops and Televisions have increased among the teenagers with the advent of online education becoming a necessity and exceed the American Academy of Pediatrics recommendations. The time devoted to social media, online gaming, and entertainment platforms leads to depressive symptoms. The blue light emanating from these devices can cause intensive damage to the metal health of teenagers. The hazards of using these devices for a longer time has an influence both on their behaviour and their rational thinking. The effect on cognitive behaviour is reflected in their mental health like loneliness, degradation of personality level and suicidal thoughts. There arises a debate to find if usage of digital gadgets for a long time has depressive symptoms in teenagers. In this chapter the feasibility of detecting depression based on the screen time usage is explored and an awareness is created to promote healthier digital habits. The NHANES, dataset contains various standardized questionnaires designed to evaluate the severity of depressive symptoms is considered for analysis. Some widely used depression scales include Beck Depression Inventory (BDI), Hamilton Rating Scale for Depression (HAM-D) etc., In this chapter, we have chosen HAM-D to assess the severity of depression in an individual. Higher scores indicate that the person is in severe depression and needs treatment. A Generalized regression neural network (GRNN) depression detection model based on screen time usage is proposed and the model is compared with Multilayer Neural Network (MLNN) Model. The proposed model gives an accuracy of 98.55