The newly-proposed Global Leadership Initiative on Malnutrition (GLIM) framework is promising to gain global acceptance for diagnosing malnutrition. However, the role of machine learning in facilitating its application in clinical practice remains largely unknown. We performed a multicenter, observational cohort study including 3998 patients with cancer. Baseline malnutrition was defined using the GLIM criteria, and the study population was randomly divided into a derivation group (n = 2998) and a validation group (n = 1000). A Classification and Regression Trees (CART) algorithm was used to develop a decision tree for classifying the severity of malnutrition in the derivation group. The model performance was evaluated in the validation group. The GLIM criteria diagnosed 588 patients (14.7%) with moderate malnutrition and 532 patients (13.3%) with severe malnutrition among the study population. The CART cross-validation identified five key predictors for the decision tree construction, including age, weight loss within six months, body mass index, calf circumference and the nutritional risk screening 2002 (NRS2002) score. The decision tree showed high performance, with an area under curve (AUC) of 0.964 (Kappa = 0.898, P<0.001, Accuracy = 0.955) in the validation group. Subgroup analysis showed that the model had apparently good performance in different cancers. Among the five predictors constituting the tree, age contributed the least to the classification power. Using the machine learning, we visualized and validated a decision tool based on the GLIM criteria that can be conveniently used to accelerate the pre-treatment identification of malnutrition in patients with cancer. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.