Stunted growth in children is a worldwide issue which may cause long term problems for individuals stunted as early as two years of age. However, predicting stunted growth with accuracy is quite complex, but machine learning poses a distinct advantage in this regard. While several techniques are available for predictive modeling, the Super Learner stands out as an ensemble method that integrates multiple algorithms into a single predictive model with enhanced performance. In this study, the Super Learner model, comprising generalized linear model, bagged trees, random forests, conditional random forest, stochastic gradient boosting, Bayesian additive regression trees, neural networks, and model averaged neural networks, achieved high performance with high area under the receiver operating characteristic curve, Brier Score, and the minimum of precision and recall values. However, after analyzing the results from cross validation, the final model selected was the Bayesian additive regression trees. Within the final model, the height-for-age z-score at one year, income, expenditure, anti-lipopolysaccharide antibody at week 6 and at week 18, plasma retinol binding protein at week 6, plasma soluble cluster designation 14 at week 18, fecal Reg 1B at week 12, vitamin D at week 18, mother’s weight and height at enrollment, fecal calprotectin at week 12, fecal myeloperoxidase at week 12, number of days of diarrhea through the first year of life, and the number of days of exclusive breastfeeding through the first year of life emerged as the top important variables for predicting stunted growth at two years of age.