<p>Early childhood education plays a crucial role in shaping a child’s future development and accurately predicting developmental outcomes is vital for personalized learning and early intervention strategies. However, existing models often struggle with integrating diverse data sources and ensuring interpretability. This study proposes a hybrid Machine Learning (ML) framework that combines Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to predict preschool children’s developmental outcomes, addressing the need for robust and interpretable models in early childhood education. The SmartLearn Preschool Dataset, which includes multimodal data such as child profiles, interaction logs, behavioral metrics and audio-visual features, is used for model training. The framework employs a stacking ensemble approach, where RF and XGBoost serve as base models and Logistic Regression (LR) is used as the meta-model. Hyperparameter optimization is performed using Optuna and (SHapley Additive exPlanations) SHAP values are incorporated for interpretability, offering insights into feature importance. The model is evaluated using several performance metrics, including accuracy, precision, recall and F1-score. The proposed model achieved an impressive accuracy of 98.78%, precision of 98.79%, recall of 98.78% and F1-score of 98.78%, with high Area Under the Curve (AUC) values for multi-class classification, demonstrating its ability to effectively predict developmental outcomes. The inclusion of SHAP values ensures interpretability, allowing educators and stakeholders to understand how specific features impact the model’s predictions. This framework offers a reliable tool for personalized education and early intervention, with broad applications in educational settings where transparency and accuracy are essential.</p>