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Early diagnosis of unilateral cerebral palsy is important after asymmetric perinatal brain injury (APBI). Our objective is to estimate the risk of unilateral cerebral palsy (UCP) in infants with APBI during the first months of life using neuroimaging and clinical assessment. Prognostic multivariable prediction modeling study including 52 infants (27 males), median gestational age 39.3 weeks with APBI from Sweden (n = 33) and the Netherlands (n = 19). Inclusion criteria: (1) neonatal MRI within one month after term equivalent age (TEA), (2) Hand Assessment for Infants (HAI) between 3.5 and 4.5 months of (corrected) age. UCP was diagnosed ≥24 months of age. Firth regression with cross-validation was used to construct and internally validate the model to estimate the risk for UCP based on the predictors corticospinal tract (CST) and basal ganglia/thalamus (BGT) involvement, contralesional HAI Each hand sum score (EaHS), gestational age and sex. UCP was diagnosed in 18 infants (35%). Infants who developed UCP more often had involvement of the CST and BGT on neonatal MRI and had lower contralesional HAI EaHS compared to those who did not develop UCP. The final model showed excellent accuracy for UCP prediction between 3.5 and 4.5 months (area under the curve, AUC = 0.980; 95% CI 0.95–1.00). Combining neonatal MRI, the HAI, gestational age and sex accurately identify the prognostic risk of UCP at 3.5–4.5 months in infants with APBI. • Infants at risk of unilateral cerebral palsy can already be identified during the first months of life. • A combination of neuroimaging and clinical assessment improves the accuracy for the prediction of unilateral cerebral palsy. • An excellent prediction model based on neonatal MRI and HAI, gestational age and sex was developed. • A nomogram for the prediction of unilateral cerebral palsy is proposed for future use in clinical practice after the model has been externally validated. [ABSTRACT FROM AUTHOR]