Huang and colleagues explored how children’s coloring performance could be used to identify their visual–motor integration (VMI) development through artificial intelligence. Visual motor integration, which involves coordinating what we see with how we move, is key to learning skills like handwriting. Traditional assessments, such as the Beery–Buktenica Developmental Test of Visual Motor Integration (VMI-4), can be time-consuming and subjective, so the researchers looked for a more efficient, engaging approach. The study involved 505 preschoolers aged 3–6 years who were asked to color a train picture. These images were analyzed using a deep learning model (EfficientNetB7) to extract visual features, which were then processed by machine learning models such as support vector machines (SVM) and eXtreme Gradient Boosting (XGBoost) to predict each child’s developmental status. After balancing the dataset with the synthetic minority oversampling technique (SMOTE), the model achieved strong predictive results: an accuracy of around 80% on test data, with a sensitivity of 0.74, specificity of 0.81, and an AUC of 0.83. The study found that a simple and enjoyable coloring task, when combined with AI, can effectively identify children at risk of developmental delay in visual motor integration. This approach could make developmental screening more accessible, especially in schools or communities with limited clinical resources, by allowing teachers or caregivers to upload coloring images through an app or webpage for immediate feedback.