Pediatric prescribing is particularly susceptible to dosing errors because many medication orders must be individualized based on weight, age, formulation, route, and frequency, and ambiguous prescription text can further obscure the intended dose, increasing the risk that an unsafe order reaches the child. Conventional clinical decision support systems typically rely on static dose-range alerts and rigid rule thresholds, which may flag obvious outliers but often lack clinical nuance and generate alerts that are difficult to interpret or justify. To address these limitations, this article proposes an explainable machine learning model for pediatric dosing error prediction that leverages prescription text and weight-based dosing rules, attributing each flagged prescription to specific contributing factors such as dose-per-kilogram deviations, formulation mismatches, or ambiguous administration instructions. The model conceptually employs a gradient-boosted tree or similarly interpretable architecture, trained on pediatric prescription records enriched with structured patient characteristics, computable dosing-rule features, and natural language processing outputs from prescription text, while SHAP-based explanations decompose each prediction into feature-level contributions that can be communicated to pharmacists and prescribers. In practice, the model could identify prescriptions likely to contain weight-based miscalculations, inappropriate formulations, or incorrect frequencies, providing explanations such as a prescribed dose exceeding the recommended weight-based range combined with a free-text instruction containing an ambiguous abbreviation. By combining predictive modeling with transparent, actionable explanations, an explainable AI approach could support safer pediatric prescribing, though prospective evaluation is needed to assess clinical usefulness, workflow integration, explanation quality, and impact on medication safety.