Abstract Background Rapid prehospital assessment of hemorrhagic shock severity is critical for trauma triage and intervention. Current non-invasive single-parameter monitoring shows limited diagnostic reliability. We developed a multi-parameter predictive model integrating mean arterial pressure (MAP), buccal mucosal CO₂ (PBUCO₂), transcutaneous oxygen (PTCO₂), and pulse pressure variation (PPV). using K-nearest neighbors (KNN) algorithm. Methods Forty-five Wistar rats were randomly divided into five groups (n = 9) with different blood loss amounts. MAP, PBUCO2, PTCO2, and PPV measurements were continuously obtained. A multi-parameter shock severity prediction model was established based on the KNN algorithm. Leave-one-out cross-validation was used to determine the value of K. Meanwhile, a prediction model based on the support vector machine (SVM) algorithm was established. The performance of the two prediction models was compared using confusion matrices and receiver operating characteristic (ROC) curve. Results When the training vs testing data set ratio is 7:3 or 6:4, and K = 3, the KNN-based model has the best prediction accuracy (94.82% and 93.51%). The confusion matrix and ROC evaluation showed that the overall performance of the KNN-based model is superior to that of the SVM-based model, at all levels of blood loss (F1 = 95.09% and 93.99%, AUC = 1 and 0.97 for the KNN-based model at 7:3 and 6:4 dataset ratio; F1 = 83.84% and 84.86%, AUC = 0.97 and 0.97 for the SVM-based model at 7:3 and 6:4 dataset ratio). Conclusions Using the detection indicators MAP, PBUCO2, PTCO2 and PPV, the KNN-based rat hemorrhagic shock severity prediction model has high accuracy and stability, and demonstrates potential feasibility for severity stratification of hemorrhagic shock in standardized preclinical models, providing a foundation for future clinical validation in prehospital environments.