This study by Keller et al. aims to analyze the risk factors and prevalence of suicidal ideation among unaccompanied young refugees (UYRs) by using machine learning. To do so, the authors trained four supervised machine-learning classifiers (logistic regression, random forest, support vector machine, and extreme gradient boosting) on data from 623 UYRs from the “Better Care” study, and then tested performance on an independent validation cohort of 94 UYRs from “porta”, an online screening platform. Predictors included age, gender, asylum status, family contact, parents alive, plus clinical measures of post-traumatic stress symptoms (PTSS), depressive symptoms, and past suicide attempts. The authors found that ML classifiers generally had good predictive performance, and accuracy ranged from ~0.734 to ~0.840, with sensitivity of ~0.857. The most relevant features for predicting suicidal ideation were past suicide attempts, PTSS, and depressive symptoms as major risk factors. Notably, having a living mother was found to be a protective factor. Keller et al. highlight that suicidal ideation is common among UYRs and that machine‐learning tools show promise in early risk detection in this population. They note that while ML approaches may help build concise risk models, limitations such as the study’s cross-sectional design and lack of generalizability call for further longitudinal research, and these results cannot be used to assume causality.