Objective To establish a Nomogram predictive model for Neonatal Sepsis （NS） based on the general characteristics and initial complete blood count of neonates. Methods Retrospective analysis was conducted on the clinical data of newborns who were admitted for the first time to NICU and completed blood routine examination after admission in the MIMIC Ⅲ database. The LASSO?Logistic regression was used to investigate the prediction factors of NS， and then Nomogram prediction model was established. Internal validation was performed using bootstrap resampling with 1000 iterations. External validation of the model was performed using the data from newborns admitted to the First Affiliated Hospital of Zhengzhou University. We evaluated the predictive performance by Area Under the Receiver Operating Characteristic Curve （AUROC）， C?index， calibration curve， and decision curve analysis （DCA）. Results Among the 3，001 neonates， 185 were diagnosed with NS. The Nomogram model was constructed based on indicators such as respiratory distress syndrome， gestational age， birthweight， and initial hematological parameters （red blood cell count， white blood cell count， lymphocyte percentage， neutrophil percentage）， exhibiting good predictive performance with an AUROC of 0.860. Satisfactory predictive abilities were confirmed through both internal and external validation. Conclusion This study developed and validated a well?performing Nomogram prediction model. With simple parameters， it can help clinicians identify newborns at high risk early.