Objective: Based on the Montreux definition, we aim to develop and validate a predictive model for the early diagnosis of neonatal acute respiratory distress syndrome. Methods: A retrospective analysis of clinical data on 198 neonates with respiratory distress from January 2018 to January 2022 was conducted. Neonates meeting Montreux definition were classified as ARDS group (n=79), while the rest were controls (n=119). Univariate analysis identified indicators for neonatal ARDS, followed by logistic regression to construct a predictive model for early diagnosis. The ability of predictors and models to predict neonatal ARDS was evaluated using AUC, and model performance was estimated through bootstrap resampling. Results: Maternal prenatal fever, abnormal fetal heart beat, meconium-stained amniotic fluid (MSAF), white blood cell (WBC)，absolute neutrophil count (ANC) , neutrophil percentage (NE%), platelet count (PLT), C-reactive protein (CRP), procalcitonin (PCT), creatine kinase (CK), activated partial thromboplastin time (APTT), serum calcium (S-Ca) and sodium（S-Na）exhibited significant differences between the ARDS group and the control group ( P<0.05). MSAF (OR=5.037; 95%CI: 1.523~16.657; P<0.05), ANC (OR=1.324; 95%CI: 1.172~1.495; P<0.05), PLT (OR=0.979; 95%CI: 0.971~0.986; P<0.05), S-Ca (OR=0.020; 95%CI: 0.004~0.088; P<0.05) emerged as independent risk factors for the development of ARDS. The respective area under the curve (AUC) values for MSAF, ANC, PLT, S-Ca, and the combined prediction models were 0.606, 0.691, 0.808, 0.761 and 0.931. Internal validation showed that the C-index for the model was 0.931. Conclusions: The predictive model that incorporates MSAF, ANC, PLT, and S-Ca demonstrates significant efficacy in predicting the early diagnosis of neonatal ARDS.