Designing and optimizing the path of diverted wells using the Particle Swarm Optimization (PSO) algorithm with the aim of achieving the optimal path length and the lowest cost is the topic addressed in this article. PSO is a computational algorithm inspired by the collective movement of some animals such as flocks of birds and fish. In designing the well path, the true measured depth (TMD) is given priority, and then other important geometric parameters such as depth to the point of deviation (DKOP), slope, azimuth, and horizontal section (HD) were considered later. The path design calculations, which were performed in the MATLAB environment, were based on a real well drilled in Egypt, which was previously designed by Shuker with a genetic algorithm and Atashnejad with classical PSO (different from the work of this article). What determines the different classical versions is the change in inertial weight. The important achievement of this research is the proposal to design the optimal path of a diverted well using the PSO method, which is itself a type of artificial intelligence method. The result can be formulated as follows: The TMD value of the Eberhart-Shee method suggests a value of 14838.08 feet, which is less predictable than the Atashnejad and Shuker methods, and is an attempt to improve the optimal path and is effective in reducing the cost of drilling a well.