Understanding the complex community mental health—built environment nexus remains vital to building healthy and sustainable cities. The recent commission report on mental health and sustainable development goals elevates concerns about understanding these complex relationships for informed-policy actions. However, there are limited analytical and methodological frameworks to unpack these relationships. Here, we develop a multi-level scenario-based predictive analytics framework (MSPAF) to address this limitation. We employ rigorously validated interpretable machine learning algorithms and scenario-based sensitivity analyses to explore the relationship between community mental health, and socio-economic/physical aspects of built environment across the US metropolitan areas. Our results suggest that declining socioeconomic conditions (e.g., poverty, low income, unemployment) are significantly associated with increased reported mental health disorders. The results also contribute to the insurance-mental health debate by showing decreased access to public health insurance is associated with increase in reported mental disorders. Finally, adults report increased mental health disorders as travel costs or housing vacancies increase, but this does not hold across all the metropolitan areas, illustrating a mixed effect of built environment’s physical aspects on mental health. We conclude by highlighting future opportunities of incorporating other micro-/macro-level data into the MSPAF framework to examine the mental health—built environment nexus further.