Background: Postpartum hemorrhage remains one of the largest causes of maternal morbidity and mortality in the United States. Objective: The aim of this paper is to use machine learning techniques to identify patients at risk for postpartum hemorrhage at obstetric delivery. Methods: Women aged 18 to 55 years delivering at a major academic center from July 2013 to October 2018 were included for analysis (N 30,867). A total of 497 variables were collected from the electronic medical record including the following: demographic information; obstetric, medical, surgical, and family history; vital signs; laboratory results; labor medication exposures; and delivery outcomes. Postpartum hemorrhage was defined as a blood loss of 1000 mL at the time of delivery, regardless of delivery method, with 2179 (7.1%) positive cases observed.