In the context of rapid urbanization, the problem of urban traffic congestion is becoming increasingly prominent. Traditional traffic control methods cannot effectively meet the traffic demand during peak hours due to their slow response speed and low efficiency. In response to the above issues, this article intends to construct an artificial intelligence based road traffic path planning method. By introducing methods such as big data analysis and machine learning, transportation path planning systems in the road network can be optimized in real time to improve traffic efficiency and alleviate congestion, thus providing new ideas for urban road traffic management. This article took solving modern transportation problems as the starting point and conducted in-depth research on road planning systems based on artificial intelligence. This article conducted in-depth research on path planning methods based on artificial intelligence (AI), and conducted simulation experiments to verify the effectiveness and feasibility of this method. On this basis, this article also optimized and improved existing path planning techniques, adopted corresponding improvement measures, and evaluated their implementation effects. Even during peak hours of simulation, the AI system maintained high efficiency in path optimization, with an average path optimization efficiency of 91%. This article provided a new approach for the development of China’s transportation industry and provided strong support for future urban transportation construction.