["Clinical Psychology &Psychotherapy, Volume 33, Issue 4, July/August 2026. ", "\nABSTRACT\nThis study estimated the prevalence of probable depression among parents and primary caregivers of children and adolescents with specific learning disorders (SLDs) in Saudi Arabia and developed explainable machine‐learning models for screening‐oriented classification. In this multicentre cross‐sectional study, 612 caregivers were recruited through clinical, psychoeducational, educational and community pathways. Children had formally documented SLD diagnoses established through qualified clinical, psychoeducational, school psychology or multidisciplinary services. Probable depression was defined as a Patient Health Questionnaire‐9 score of 10 or higher. Forty‐seven candidate predictors covered parent, child, family, psychosocial, socio‐economic, stigma‐related and service‐access domains. Elastic‐net logistic regression, random forest, XGBoost, LightGBM and a soft‐voting ensemble were evaluated using stratified 10‐fold cross‐validation. Probable depression was identified in 218 participants (35.6%; 95% CI 31.8–39.6). XGBoost and the soft‐voting ensemble achieved the highest ROC AUC (0.916); XGBoost yielded an average precision (AP) of 0.867 and a Brier score of 0.115. At a screening‐oriented threshold of 0.425, XGBoost achieved sensitivity of 0.858, specificity of 0.830 and negative predictive value of 0.913. SHAP analyses identified parenting stress, parental anxiety symptoms, sleep quality, SLD severity, child emotional and behavioural difficulties, perceived social support and coping capacity as the most influential predictive features. Probable depression was common in this service‐ and community‐recruited sample. Explainable models may support family‐centred identification and referral, but external evaluation is required before implementation.\n"]