This article explored a distribution network path planning method based on artificial intelligence (AI) and optimization algorithms (OAs) to solve multiple problems in traditional research. Traditional methods have limited effectiveness in dealing with complex network structures and dynamic load changes, high computational complexity, and low energy utilization efficiency. To address these challenges, firstly, multiple algorithms were compared and analyzed, and genetic algorithm was identified as the main OA, combined with PSO’s (Particle Swarm Optimization) local search capability for hybrid optimization. Then, this article designed a distribution network path planning strategy based on real-time data and intelligent algorithms, aiming to improve the efficiency of power transmission and energy utilization, and reduce system operating costs. By flexibly adjusting and dynamically optimizing, the distribution network can respond more quickly to changes in load demand, enhancing the overall response capability and stability of the system. In addition, this article also focused on improving the security and reliability of the system, especially whether it can quickly make adaptive adjustments and response measures in the face of emergencies or abnormal situations, in order to ensure the continuous stability of the power grid operation. Finally, the actual effectiveness and application potential of AI and OA in distribution network path planning can be verified. By introducing AI and OA such as genetic algorithm and PSO, significant improvements have been made in the transmission efficiency of distribution networks. Specifically, after optimization, the average transmission efficiency increased by about 0.15%, with an improvement rate of about 21.43%. The total network loss was significantly reduced, with an average reduction rate of about 33.33%. The system’s responsiveness and stability have been improved, and the optimized data is more centralized and stable. The effectiveness of OAs in reducing operating costs and emphasizing their role in improving the economic efficiency of the power system.
