Proper nutrition is one of the necessities of human health. Malnutrition in children is an ecumenical problem that causes mortality and morbidity in children. Afghanistan is one of the countries which suffers from child severe acute malnutrition. In this study, a machine learning based model was proposed to predict the severity of edematous malnutrition in children between the age of 1-59 months in the context of Afghanistan. Random Forest, J48, and Naïve Bayes classifiers were applied to the malnutrition-related data, which was collected from two hospitals in Afghanistan. The Random Forest technique obtained the highest accuracy and the performance of J48 was also moderate. This study explores how machine learning classification techniques can classify the edematous malnutrition in U5 children. Overall, the findings of the proposed method demonstrate that our model that obtained the robust results provides a potential mechanism for the prediction of nutritional oedema using machine learning classification algorithms for Af-ghan under-five children.