Mental health disorders affect millions of people all around the world posing a significant threat to mental health. The effects of these disorders impact an individual’s total well-being and quality of health. This work explores the potential of machine learning frameworks to predict treatment-seeking behavior of an individual by assessing various environmental factors. The paper explores the efficacy of ensemble modeling for its potential to predict the risk of mental health issues. It also analyzes the relationship between the primary factors causing the disorder. When evaluating risk, the decision tree model demonstrated an accuracy of 83%, indicating the potential of machine learning models in identifying people who might benefit from early detection and support. Considering the steep rise in the number of individuals affected by depression and mental illnesses, identification and intervention at the right time are of paramount importance. This study emphasizes the role of machine learning frameworks to identify individuals at risk of mental disorders and provide transformative healthcare.