Objective To identify prognostic factors for H3K27 mmutant diffuse midline glioma (DMG) and to develop and validate a nomogram for predicting poor prognosis. Methods Patients with histologically confirmed H3K27Mmutant DMG recorded in the Surveillance Epidemiology and End Results database from 2000 to 2019 were retrospectively included. Cases were randomly split into a training set (n=97) and a validation set (n=41) at a 7:3 ratio. Candidate variables were screened by four machinelearning approaches-extreme gradient boosting random forest, least absolute shrinkage and selection operator regression, and decision tree. Multivariate Cox regression models were then used to develop predictive models. Risk factors identified by multiple methods were further tested by multivariate Cox regression to confirm independent prognostic value. A nomogram to predict 6, 12, and 18month overall survival was constructed from the independently significant predictors. Model discrimination, calibration, and clinical utility were evaluated using area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). Survival differences were assessed by Kaplan-Meier analysis. Results The four machinelearning methods produced overlapping but not identical sets of candidate predictors; five variables overlapped across methods: Age, tumor size, WHO grade, laterality, and radiation. Multivariate Cox regression identified four independent adverse prognostic factors: Age >60 years (HR=3.018; 95%CI: 1.15-7.92; P=0.025), larger tumor size (per unit increase) (HR=1.039; 95%CI: 1.01-1.06; P=0.004), higher WHO grade (HR=2.057; 95%CI: 1.21-3.49; P=0.008), and midline location (HR=2.101; 95%CI: 1.32-3.34; P=0.002). Radiotherapy was independently associated with improved survival (protective effect: HR=0.410; 95%CI: 0.23-0.75; P=0.004). The nomogram incorporating these factors demonstrated AUCs for 6, 12, and 18 month OS of 0.647, 0.746, and 0.625 in the training set and 0.632, 0.725, and 0.725 in the validation set, respectively, indicating acceptable discrimination. Calibration plots showed good agreement between predicted and observed survival probabilities, and DCA indicated favorable clinical utility. Conclusion A nomogram developed from machine learning-selected predictors reliably estimates shortterm OS in patients with H3K27 mmutant DMG. This model may help clinicians identify highrisk patients and tailor individualized treatment strategies.