<p>Air pollution remains a pressing global concern, significantly contributing to climate change and environmental degradation. Among the primary contributors, greenhouse gases—especially from carbon emissions—play a critical role in the warming of the Earth’s atmosphere. In urban areas, vehicular emissions are the second-largest source of carbon emissions, following industrial outputs. This study quantifies the carbon footprint of urban intersections by analyzing carbon monoxide (CO) emissions at signalized intersections, where vehicular idling and acceleration are concentrated. Three four-way signalized intersections in Hyderabad, India, were selected for an in-depth analysis of CO levels in relation to traffic parameters, namely nearing traffic volume (NV), red signal duration (RT), queue length during red time (QLR), and intersection area (IA). CO emissions were measured using an electrochemical carbon monoxide meter (HTC), which provided real-time CO concentration data. Traffic parameters were collected via videography and analyzed using the DATAFROMSKY software to extract vehicle counts and movements. The study applied Multiple Linear Regression (MLR) and Support Vector Regression (SVR) models to predict CO levels at these intersections. Performance assessments using RMSE and MAPE indicated that the SVR model outperformed MLR, achieving an RMSE of 1.12 and a MAPE of 0.085. Furthermore, intersections were ranked based on the National Air Quality Index (AQI), with all three sites falling within the “Poor” category, registering AQI values of 207, 276, and 276, respectively. This study highlights the critical need for targeted interventions at urban intersections to mitigate CO emissions and improve air quality in alignment with sustainable environmental objectives.</p>