To realize the fault early warning function of healthcare medical equipment, this study constructs an equipment fault early warning model and combines particle swarm optimization and long-short term memory network to test the performance. The study obtains the optimal value of data feature vectors through particle swarm optimization algorithm and uses longshort term memory prediction model to predict and classify feature signals. In addition, the study uses the binning method to denoise the collected data and normalizes the denoised data so that each feature data was distributed between 0 and 1. The results showed that the fitting between actual values and predicted values was good. The maximum values of Precision, Recall, and F1 of the designed warning model were 97.98%, 97.82%, and 97.68%, respectively, which were significantly better than the control model. This indicated that the warning model designed by the research had good performance. The combination of the particle swarm optimization algorithm and the long short-term memory network model offered unique advantages in the medical field. The particle swarm optimization algorithm could efficiently identify key features, avoid local optima, and improve the model’s generalization ability. Long short-term memory networks could accurately capture the dynamic trends of faults and adapt to the temporal nature of medical data. Combining the two could meet the highprecision, real-time, and adaptive requirements of medical equipment fault warnings, effectively improving their accuracy.