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Abstract We aimed to develop a risk scoring system for 1‐week and 1‐month mortality after major non‐cardiac surgery, and assess the impact of postoperative factors on 1‐week and 1‐month mortality using machine learning algorithms. We retrospectively reviewed the medical records of 21,510 patients who were transfused with red blood cells during non‐cardiac surgery and collected pre‐, intra‐, and postoperative features. We derived two patient cohorts to predict 1‐week and 1‐month mortality and randomly split each of them into training and test cohorts at a ratio of 8:2. All the modeling steps were carried out solely based on the training cohorts, whereas the test cohorts were reserved for the evaluation of predictive performance. Incorporation of postoperative information demonstrated no significant benefit in predicting 1‐week mortality but led to substantial improvement in predicting 1‐month mortality. Risk scores predicting 1‐week and 1‐month mortality were associated with area under receiver operating characteristic curves of 84.58% and 90.66%, respectively. Brain surgery, amount of intraoperative red blood cell transfusion, preoperative platelet count, preoperative serum albumin, and American Society of Anesthesiologists physical status were included in the risk score predicting 1‐week mortality. Postoperative day (POD) 5 (neutrophil count × mean platelet volume) to (lymphocyte count × platelet count) ratio, preoperative and POD 5 serum albumin, and occurrence of acute kidney injury were included in the risk score predicting 1‐month mortality. Our scoring system advocates the importance of postoperative complete blood count differential and serum albumin to better predict mortality beyond the first week post‐surgery.