Objective This study aimed to explore the predictive value of multifactor logistic regression and XGBoost models for oral infections during radiotherapy in tongue cancer patients.Methods A total of 431 tongue cancer patients receiving radiotherapy at the First Affiliated Hospital of Xinjiang Medical University from January 2003 to December 2022 were randomly divided into a training group (n = 288) and a prediction group (n = 143). The predictive performance of multifactor logistic regression and XGBoost models for oral infections during radiotherapy in tongue cancer patients was compared.Results The results of the multifactor logistic regression model showed that age [O^R = 3.250 (95% CI: 1.476, 7.634) ], tumor stage [O^R = 2.941 (95% CI: 1.248, 7.613) ], oral environment [O^R = 0.210 (95% CI: 0.079, 0.502)], surgery status [O^R = 0.285 (95% CI: 0.113, 0.663) ], hemoglobin [O^R = 0.323 (95% CI: 0.139, 0.712)], and serum albumin [O^R = 0.353 (95% CI: 0.148, 0.851) ] were independent predictors of oral infections during radiotherapy. The XGBoost model identified oral environment, surgery status, tumor stage, serum albumin, age, concurrent chemotherapy, red blood cell count, hemoglobin, and neutrophil count as important predictors. The area under the receiver operating characteristic curve (AUC) for the multifactor logistic regression model and the XGBoost model were 0.830 and 0.835, respectively, with no statistically significant difference between them (P > 0.05). Sensitivity was 88.24% (95% CI: 0.729, 1.000) and 82.35% (95% CI: 0.642, 1.000), while specificity was 68.25% (95% CI: 0.601, 0.764) and 69.84% (95% CI: 0.627, 0.786) for the multifactor logistic regression and XGBoost models, respectively.Conclusion Both multifactor logistic regression and XGBoost models have significant predictive value for oral infections during radiotherapy in tongue cancer patients, with comparable predictive performance. Establishing these models can help identify high-risk individuals for oral infections, enabling early preventive measures and reducing the risk of oral infections.