Urban air pollution caused by vehicular emissions remains one of the most pressing environmental challenges, negatively affecting both public health and climate processes. In Kokshetau, Kazakhstan, where electric vehicle (EV) adoption accounts for only 0.019% of the total fleet and charging infrastructure is nearly absent, reducing transport-related emissions requires short-term and cost-effective solutions. This study proposes an integrated approach combining urban ecology principles with computational modeling to optimize traffic signal control for emission reduction. An artificial neural network (ANN) was trained using intersection-specific traffic data to predict emissions of carbon monoxide (CO), nitrogen oxides (NO_x), sulfur dioxide (SO₂), and particulate matter (PM_2.5). The ANN was incorporated into a nonlinear optimization framework to determine traffic signal timings that minimize total emissions without increasing traffic delays. The results demonstrate reductions in emissions of CO by 12.4%, NO_x by 9.8%, SO₂ by 7.6%, and PM_2.5 by 10.3% at major congestion hotspots. These findings highlight the potential of the proposed framework to improve urban air quality, reduce ecological risks, and support sustainable transport planning. The method is scalable and adaptable to other cities with similar urban and environmental characteristics, facilitating the transition toward eco-friendly mobility and integrating data-driven traffic management into broader climate and public health policies.