Objective To construct a risk prediction model for in-hospital mortality in neonates with severe asphyxia. Methods A total of 836 neonates with severe asphyxia admitted to 20 tertiary hospitals in Henan Province between January 2022 and December 2025 were retrospectively enrolled and divided into a death group (n=267) and a survival group (n=569) according to outcome. Birth characteristics, resuscitation measures, and early complications were analyzed. Multivariable logistic regression was used to identify risk factors, and a nomogram prediction model was developed. Receiver operating characteristic curves, calibration curves, and decision curve analysis were used to evaluate the model's discrimination, calibration accuracy, and clinical applicability. Results Multivariable logistic regression showed that high lactate level, low birth weight, a 5-minute Apgar score of ≤5, use of epinephrine, volume expansion during resuscitation, persistent pulmonary hypertension of the newborn, pulmonary hemorrhage, shock, and grade III/IV intraventricular hemorrhage were independent risk factors for in-hospital mortality in neonates with severe asphyxia (P<0.05). The nomogram based on these risk factors yielded an area under the curve of 0.795 (95%CI: 0.762-0.827). Calibration and decision curve analyses indicated that the model had high accuracy and good clinical applicability. Conclusions In-hospital mortality among severely asphyxiated neonates is closely associated with birth weight, resuscitation modalities, and early complications such as pulmonary hemorrhage and shock. The risk model constructed based on these factors shows good predictive value.