OBJECTIVE: Selective screening for gestational diabetes mellitus (GDM) remains a widespread strategy. Variation in criteria identifying at-risk women questions its accuracy, with implications for clinical outcome and resource allocation. Our aim was to develop and externally validate a multivariable prediction model with improved performance compared to the current French pre-screening selection strategy. METHODS: Data was derived from the population-based 2021 (derivation sample) and 2016 (external validation sample) French National Perinatal Survey (ENP). Independent predictors of GDM were identified using a multivariable logistic regression model. Predictive performance was assessed through the area under the receiver operating characteristic curve. Diagnostic performance was assessed through sensitivity, specificity, and accuracy. Sensitivity analyses were conducted: (1) in maternity centers with quasi-universal screening, (2) outcome strictly defined as GDM cases associated with large-for-gestational-age births and (3) implementing doubly robust estimators of sensitivity and specificity. RESULTS: The study population included 10834 women in the derivation sample and 11633 in the validation sample, where the prevalence of GDM was respectively 19.4% and 13.2%. Maternal age, body mass index, obstetric history, family history of diabetes, and maternal country of birth were independent predictors of GDM. The prediction model demonstrated a statistically significant improvement in specificity (0.49 [95% CI: 0.48-0.50] vs. 0.46 [95% CI: 0.45-0.47]) and overall diagnostic accuracy (0.53 [95% CI: 0.52-0.54] vs. 0.50 [95% CI: 0.49-0.51]). CONCLUSION: The prediction model modestly improved performance while maintaining the same screening rate, though it is unlikely to justify additional complexity of implementation, therefore limiting its added-value in clinical practice. These findings suggest that available clinical predictors already capture most of the predictive information relevant for selective screening.