Background: Perinatal arterial ischemic stroke (PAIS) is a focal brain injury in term neonates, identified postnatally but presumed to occur around birth. Early risk detection and targeted treatments are limited. We developed and validated a diagnostic risk prediction model from common clinical factors to predict a term neonate’s probability of PAIS. Methods: A diagnostic prediction model was developed using multivariable logistic regression. Common pregnancy, delivery, and neonatal clinical factors were collected across four registries. Variable selection was based on peer-reviewed literature. Participant inclusion criteria were term birth and no underlying predisposition to stroke. The primary outcome was discriminative accuracy of the model predicting PAIS, measured by the concordance (C-) statistic. Results: 2571 participants (527 cases, 2044 controls) were eligible for analysis. Nine variables were included in the model – maternal age, tobacco exposure, recreational drug exposure, pre-eclampsia, chorioamnionitis, maternal fever, emergency c-section, low 5-minute Apgar score, and sex – to predict the risk of PAIS in a term neonate. This model demonstrated good discrimination between cases and controls (C-statistic 0.73) and model fit (Hosmer-Lemeshow p=0.20). Conclusions: Clinical variables can be used to develop and internally validate a model of PAIS risk prediction. Identifying high-risk neonates for early screening and treatment could reduce lifelong morbidity.