Pedestrian streets, as emerging urban structures, have made life more convenient but also pose significant fire risks. To address the problem of evacuating people during sudden fire incidents in commercial pedestrian streets, a path planning algorithm based on an improved ant colony algorithm in a smoky environment is proposed. Firstly, the Gaussian plume model is used to calculate the concentration of smoke on the roads. Based on this, the equivalent distance is used instead of the Euclidean distance to quantify the harm of gases to humans. At the same time, a modified heuristic function is proposed by considering the impact of crowd density on speed. Given that traditional ant colony algorithms exhibit slow convergence, a tendency to get trapped in local optima, and an excess of redundant nodes in path planning, the A* algorithm is further integrated to adjust the initial pheromone concentration of the ant colony algorithm. The path selection and pheromone update rules are also improved, and a deadlock prevention mechanism is introduced, enhancing the global search capability and increasing search efficiency. Finally, the obtained path is smoothed to reduce the extra path length caused by redundant nodes. Simulation experiments have validated that the algorithm not only significantly improves performance but also effectively plans escape routes according to the fire environment.