Recognizing traffic signs is very important in the field of intelligent transportation systems, because it effectively increases the safety of our roads. By equipping drivers with vital information about potential hazards, this technology significantly reduces response time and helps making quick decisions while operating the vehicle. Due to the exponential growth of our population, unfortunately the increase in the number of vehicles has led to a painful increase in traffic fatalities and injuries. Although the expansion of the transportation network helps the traffic issue, this expansion may be inapplicable due to the High financial costs, geographic and environmental limitations, as well as a long period of time to improve transportation infrastructure. Quality control of traffic colors, shabrangs, glass beads and other traffic signs in accordance with national and international standards will significantly reduce the number of dead and injured. Managing a smart city with emphasis on waste reduction and efficient use of resources is the main responsibility of traffic and transportation activities. The use of complex processing techniques in cars to increase safety without interfering with driving has been proposed as part of the concept of a smart city. This study looks at a new approach that can help create a framework for placing street signs using Google images into a traffic management program. The findings show a reduction in travel time, an increase in the use of the fleet, better maintenance of vehicles, and a reduction in carbon dioxide emissions from passenger cars.