Current air pollution control predominantly employs reactive measures like emergency factory closures and production restrictions, which may disrupt socioeconomic activities while lacking sustainability. To address this limitation, we propose proactive urban planning interventions for long-term synergistic PM2.5 and ozone (O3) control through the development of the city-level urban form regulation-aided air quality optimization model (CUFR-AQOM). The model utilizes GeoDetector to identify global drivers of air pollution, followed by hybrid stepwise regression and geographic weighted regression to establish localized mapping relationships between air quality and urban form indicators (UFIs). Subsequently, it designs regulatory pathways incorporating regulatable UFI thresholds and air quality optimization targets. Validation across 274 Chinese cities (2005–2020) showed the model’s robust predictive performance, achieving coefficients of determination (R2) of 0.88 and 0.89 for PM2.5 and O3, and root mean squared errors (RMSE) of 5.09 μg/m3 and 4.71 μg/m3 for PM2.5 and O3, respectively. Projections for the post-regulation scenario revealed a notable reduction in the number of cities exceeding secondary standards for PM2.5 (from 141 to 94) and O3 (from 46 to 31), underscoring the effectiveness of UFIs regulation in synergistic PM2.5 and O3 control. This study contributes a novel decision-making framework for air quality-friendly urban planning and provides suggestions for tailored regional regulation.