Despite the increase in accessibility of efforts to improve individuals’ well-being, mental health has become an increasingly problematic issue in the United States. Notably, following the COVID-19 pandemic, a large proportion of high school students have reported frequently experiencing feelings of sadness and hopelessness, in line with the deterioration of mental health, every few weeks. The methods proposed to solve this glaring issue rely on helpful, though highly rudimentary, telemedicine services and face-to-face sessions with psychologists. While effective, these sessions tend to be limited by their in-person nature and obstructed by financial barriers, compounding student-held stigma. This research project investigates the effectiveness of novel statistics and machine learning advances in quickly discerning anxiety and depression, two of the most pertinent mental health illnesses among young adults. Through the correlative analysis of the Substance Abuse and Mental Health Services Administration’s (SAMHSA) mental health (MH-CLD) datasets from 2019 and 2021, the project aims to create a machine learning framework, taking into account demographic background and “life stressors,” to recognize these illnesses with the Keras API.