This study investigates the application of Artificial Intelligence (AI) and sensitivity analysis in predicting shortages of pharmaceuticals and medical equipment during health crises. The primary issue addressed in the research is the shortage of drug and medical equipment resources during crises, which can have a significant negative impact on hospital efficiency and mortality rates. This research aimed to develop an AI-based prediction model for simulating and forecasting resource shortages in crises, as well as to perform sensitivity analysis to identify factors affecting the accuracy of predictions. The research methodology employed AI models, including neural networks and linear regression, to predict shortages of pharmaceuticals and medical equipment. Additionally, sensitivity analysis was used to simulate various crisis scenarios. The findings revealed that factors such as transportation disruptions, demand fluctuations, and seasonal changes have a significant impact on the accuracy of predictions. The results of this study suggest that AI models and sensitivity analysis can effectively assist in improving the prediction and management of pharmaceutical and medical equipment resources during health crises.