<p>Traffic congestion at urban intersections, driven by static traffic light systems, leads to increased vehicle idling, fuel consumption, greenhouse gas emissions, and significant environmental impacts. To address this issue, the present study proposes a Fuzzy Logic-based Green Light Optimal Speed Advisory (GLOSA) system leveraging Vehicle-to-Infrastructure (V2I) communication to optimize traffic flow and reduce environmental impacts. The Fuzzy Logic Green Light Optimal Speed Advisory System retrieves real-time data on road speed limits, distance to the traffic light, and signal phase timing, using Fuzzy Logic to estimate an optimal vehicle speed that minimizes stops and idling. We started by simulating the proposed system using the Simulation of Urban MObility (SUMO) simulation tool under various scenarios. Then, we compared static and adaptive traffic light configurations with conventional and Fuzzy Logic GLOSA approaches using real-world traffic data. Finally, the results show a remarkable fuel consumption and emissions (CO, CO₂, NOx, NMVOC, PM) saving using the Fuzzy Logic model compared to the standard model under both traffic light regulation strategies. The Fuzzy Logic GLOSA outperformed the standard GLOSA by 11% and 13%, respectively, under both static and adaptive traffic lights. Additionally, the adaptive traffic control achieves 26% emission reductions over static signals, while the combined adaptive-FL-GLOSA system delivers 47% total reductions, demonstrating complementary effects of this approach. These findings highlight the potential of the proposed combined approach to improve urban mobility and sustainability, offering a scalable solution for smart cities. Future work will explore real-world implementation challenges and system scalability across diverse urban environments.</p>