Urban mental wellbeing has emerged as a critical public health concern as more than half of the global population now lives in cities. Environmental factors within urban settings, such as air quality, access to greenspaces, and mobility infrastructure, directly influence residents’ psychological mental health outcomes. Cities worldwide struggle to systematically assess how their environmental conditions affect population-level mental wellbeing risk. Without standardized methods to measure these risks, urban planners and policymakers lack the evidence-based tools needed to prioritize resources in environmental infrastructure that could improve urban mental wellbeing for millions of urban residents. Current approaches to assessing urban mental wellbeing typically rely on clinical trials, surveys, or subjective self-reporting methods that are resource-intensive, time-consuming, and difficult to scale across different cities. Existing smart city data infrastructure captures extensive environmental information through monitoring stations, geographic databases, and transit records, yet this data remains underutilized for mental wellbeing assessment purposes. This research develops a computational framework that transforms established scientific findings about environmental exposure and urban mental wellbeing in a systematic approach using data that smart cities already collect. The computational framework focuses on three environmental contextual cues that research has consistently linked to mental wellbeing outcomes: air quality measured through PM2.5 concentrations, accessibility to greenspaces accessability, and proximity to public transit infrastructure. Each contextual cue is evaluated using literature-derived thresholds that define risk levels, converting raw environmental measurements into standardized risk scores. The methodology applies these risk assessments at the adminsitrative level across entire urban populations, then aggregates scores using population weighting to generate neighborhood-level and city-level indices. This approach produces the Urban Mental Wellbeing Index (UMWI), which provides a quantitative measure of environmental exposure risk to urban mental wellbeing. The computational framework was validated through implementation in three diverse cities: Chicago, Manchester, and Portland. These cities differ substantially in population density, geographic characteristics, climate conditions, and data infrastructure, yet the framework successfully processed data from all three contexts using consistent analytical procedures. Results demonstrated that greenspace accessibility emerged as the primary driver of overall risk variation, with near-perfect correlation to final UMWI scores. Chicago exemplified how spatial distribution matters more than aggregate provision, providing 139.2 square meters per person yet leaving 90% of residents beyond the protective 300-meter threshold for stress recovery benefits. This research contributes a scalable, replicable methodology that smart cities can implement using their existing data infrastructure without requiring new data collection or specialized survey instruments. By converting established research findings into operational metrics, this work bridges the gap between academic knowledge about urban mental wellbeing and practical smart city applications. The computational framework provides cities with a diagnostic tool to assess their current state, identify priority areas for improvement, and monitor the effectiveness of environmental interventions over time, ultimately supporting evidence-based decisions that could improve mental wellbeing outcomes for urban populations worldwide.