Objective: A risk stratification system for tailoring treatment selection is absent for patients with pancreatic ductal adenocarcinoma (PDAC). Machine learning models can outperform traditional survival models in predicting outcomes and guiding treatment. Therefore, the current study aimed to test the performance of machine learning models in predicting disease-free survival (DFS) and overall survival (OS) in operated PDAC cases. Material and Methods: The demographic, clinical, histopathological, radiologic, and laboratory data for the resected PDAC samples were retrospectively reviewed. Univariate and multivariate conventional survival analyses were conducted for the 6- month DFS and 12-month OS. Two machine learning methods were adopted: a machine learning model, DeepHit, and a gradient boosting decision tree model, LightGBM (Light Gradient Boosting Machine). The performance of these models was compared using the area under the receiver operator characteristic curves (AUROC). Results: For the study, 121 PDAC cases that underwent resection surgery with curative intent were included. The median OS of the study population was 21.9 (11.5-44.4) months, and the median DFS was 11.8 (6-25.6) months. The constructed deep learning model AUROC values were as follows: Relapse at 6 months 0.58 (±0.177) and 0.73 (±0.098); survival over 12 months 0.56 (±0.14) and 0.78 (±0.078); survival over 24 months 0.53 (±0.13), and 0.63 (±0.083). Conclusion: Machine learning models performed similarly to the Cox regression-based dichotomous models. However, further validation of the model in a different and larger dataset is required.