Background: We externally validated Fujimoto's post-transplant lymphoproliferative disorder (PTLD) scoring system for risk prediction by using the Taiwan Blood and Marrow Transplant Registry Database (TBMTRD) and aimed to create a superior scoring system using machine learning methods. Materials and Methods: Consecutive allogeneic hematopoietic cell transplant (HCT) recipients registered in the TBMTRD from 2009 to 2018 were included in this study. The Fujimoto PTLD score was calculated for each patient. The machine learning algorithm, least absolute shrinkage and selection operator (LASSO), was used to construct a new score system, which was validated using the fivefold cross-validation method. Results: We identified 2,148 allogeneic HCT recipients, of which 57 (2.65%) developed PTLD in the TBMTRD. In this population, the probabilities for PTLD development by Fujimoto score at 5 years for patients in the low-, intermediate-, high-, and very-high–risk groups were 1.15%, 3.06%, 4.09%, and 8.97%, respectively. The score model had acceptable discrimination with a C-statistic of 0.65 and a near-perfect moderate calibration curve (HL test p =.81). Using LASSO regression analysis, a four–risk group model was constructed, and the new model showed better discrimination in the validation cohort when compared with The Fujimoto PTLD score (C-statistic: 0.75 vs. 0.65). Conclusion: Our study demonstrated a more comprehensive model when compared with Fujimoto's PTLD scoring system, which included additional predictors identified through machine learning that may have enhanced discrimination. The widespread use of this promising tool for risk stratification of patients receiving HCT allows identification of high-risk patients that may benefit from preemptive treatment for PTLD. Implications for Practice: This study validated the Fujimoto score for the prediction of post-transplant lymphoproliferative disorder (PTLD) development following hematopoietic cell transplant (HCT) in an external, independent, and nationally representative population. This study also developed a more comprehensive model with enhanced discrimination for better risk stratification of patients receiving HCT, potentially changing clinical managements in certain risk groups. Previously unreported risk factors associated with the development of PTLD after HCT were identified using the machine learning algorithm, least absolute shrinkage and selection operator, including pre-HCT medical history of mechanical ventilation and the chemotherapy agents used in conditioning regimen.