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.