Objective: To integrate conventional imaging features on abdominal magnetic resonance imaging (MRI) with clinical characteristics to construct a machine learning predictive model, and to evaluate the predictive value of tumor necrosis of pancreatic ductal adenocarcinoma (PDAC) in the prognosis. Methods: This study enrolled a total of 139 patients with pathologically confirmed pancreatic ductal adenocarcinoma (PDAC). On conventional MRI images, the presence or absence of tumor necrosis was determined. Conventional MRI features and clinical characteristics associated with survival were screened, and both a Cox proportional hazards model and a random forest survival model were established to analyze the predictive value of necrosis for the prognosis of PDAC patients. The performance of the models was evaluated using the C-index, time-dependent receiver operating characteristic (ROC) curves, calibration curves, and Kaplan-Meier curves. Results: There were statistically significant differences in gender and survival time between the necrosis group and the non-necrosis group of patients as determined on conventional MRI images (P<0.05). Multivariate analysis revealed that age, tumor size, necrosis proportion, and whether surgery was performed were independent risk factors for overall survival (OS). The random forest model demonstrated superior performance in predicting the overall survival rate of PDAC patients, with a C-index of 0.758 (training group) and 0.712 (testing group). The area under the curve (AUC) values for 1-, 2-, and 3-year overall survival rates were 0.739, 0.716, and 0.693 in the training group, and 0.704, 0.679, and 0.647 in the testing group, respectively. The results indicated that machine learning models exhibit better predictive capabilities compared to conventional models. Conclusion: Tumor necrosis determined on abdominal MRI images has predictive value for patient prognosis. The random forest model can more effectively distinguish between high-risk and low-risk patient groups.