Predictive models may help in determining the risk/benefit ratio of allogeneic hematopoietic stem cell transplantation (HSCT) in acute leukemia (AL). Using a machine-learning algorithm we have previously developed the AL- European Society for Blood and Marrow Transplantation (EBMT) score for prediction of mortality following transplantation. We report here the first external validation of the AL-EBMT score in a cohort of AL patients from the Italian national transplantation network. A total of 1848 patients transplanted between the years 2000-2014 were analyzed. The median age was 45.9. Indications for HSCT were Acute Myeloid Leukemia (68.1%) and Acute Lymphoblastic Leukemia (31.9%). The majority of patients were in first complete remission (60.4%), and received myeloablative conditioning (81.3%). Median follow-up was 2 years. The score was well-calibrated for prediction of day 100 mortality and 2-year overall survival (OS), leukemia free survival (LFS), and nonrelapse related mortality, with corresponding area under the receiver-operator curves of 0.698, 0.651, 0.653, and 0.651, respectively. Increasing score intervals were associated with a decreasing probability of 2-year OS and LFS. The highest scoring group was associated with a hazard ratio of 3.16, 2.8, and 2.27 for 2-year OS, LFS, and NRM, respectively. In conclusion, the AL-EBMT score identified three distinct risk groups and was predictive of OS. It is a valid tool for stratifying the risk of acute leukemia patients undergoing allogeneic HSCT.