<![CDATA[Stunting remains a major public health problem thatrequires early identification and continuous growth monitoring. This study developed an artificial intelligence-based model for stunting risk prediction and child growth monitoring using anthropometric data. The proposedframework integrates stunting detection, growthmonitoring, risk prediction, nutrition analysis, and healtheducation through an AI Engine consisting of data input,data processing, feature extraction, AI modeling, and riskoutput. A total of 60 child records were evaluated using four anthropometric configurations: Weight-for-Age, Height-for-Age, Head Circumference-for-Age, and a combination of Weight, Height, and Head Circumference according to age.The results showed accuracies of 85.1%, 86.0%, 90.6%, and82.3%, respectively. Head Circumference-for-Age achievedthe highest accuracy of 90.6% in the evaluated dataset.These findings demonstrate the potential ofanthropometric data and artificial intelligence forsupporting stunting risk prediction and child growthmonitoring. Further validation using larger and longitudinaldatasets is required to establish model robustness andgeneralizability.]]>
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