Millimeter wave radar is a commonly used non-contact ranging technology. Due to environmental factors and various errors in the measurement process, the ranging results may have certain errors. Studying error compensation methods can effectively improve the ranging accuracy of millimeter wave radar, thereby obtaining more accurate distance information of target objects. Therefore, a high-precision millimeter wave radar ranging signal error compensation method based on machine learning is proposed. The noise in the radar ranging signal is removed through a Gaussian filter, and the signal denoising process is completed. The distance and angle information of the target object is measured using simulated insertion pulse counting method and four quadrant spot positioning method. The particle swarm optimization method is optimized through adaptive inertia weight and convergence factor, and the optimized particle swarm algorithm is used to improve the BP neural network, by inputting the measured distance and angle information into an improved BP neural network for training, the compensated radar ranging signal can be obtained. The experimental results show that the signal processing effect of this method is good, and the azimuth and elevation errors of the compensated millimeter wave radar ranging signal are close to 0, and the signal smoothness is high.