〔Abstract〕 Objective To develop and validate a diagnostic prediction model for differentiating pulmonary tuberculosis from community-acquired pneumonia based on routine blood parameters. Methods A total of 642 patients with pulmonary tuberculosis and 503 patients with community-acquired pneumonia were retrospectively enrolled from the Seventh Hospital of Tangshan. They were randomly divided into a training set and an internal validation set at a 7:3 ratio. Additionally, 218 patients from the 981st Hospital were independently included as an external validation set. The Boruta algorithm and recursive feature elimination were employed to select predictors from 82 blood parameters, sex, and age. A multivariate Logistic regression model was established and evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. Results The final model incorporated eight predictors, namely uric acid, urea nitrogen, alkaline phosphatase, monoamine oxidase, alanine aminotransferase, glutathione, neutrophil percentage, and age. The model achieved areas under the curve (AUCs) of 0.800 (95%CI: 0.769-0.830), 0.787 (95%CI: 0.738-0.836), and 0.736 (95%CI: 0.667-0.835) in the training, internal validation, and external validation sets, respectively. The model demonstrated good calibration, and decision curve analysis showed clinical net benefit within the threshold probability range of 10%- 80%. Conclusion The developed model exhibits good discriminative ability and clinical utility, serving as an effective early screening tool for primary health care institutions.