Untreated neonatal jaundice can have severe consequences. Effective screening for neonatal jaundice can prevent long-term complications in infants. Non-invasive approaches may be beneficial in settings with limited resources. This feasibility study explores a texture-based machine learning approach for early detection of neonatal jaundice. Clinical data and skin images of 200 infants were captured from four body locations using the Neonatal Jaundice Screening and Assessment Plate. Data were split into training/validating (n = 160) and blind testing (n = 40) datasets. Ninety-two features (three clinical, 89 texture-based) were extracted after image processing. Eight machine learning models were compared for bilirubin level prediction. The best performing model, Support Vector Machine (SVM), was implemented in a web-based application (AmberSNAP) and tested using blind testing dataset. SVM paired with RRelief-F feature selection achieved optimal performance for head and sternum measurements, while SVM with Univariate Regression performed best for abdomen and lower leg measurements. Blind testing demonstrated good performance in bilirubin level prediction (mean absolute error: 1.675 mg/dL
root mean square error: 2.192 mg/dL), with moderate correlation between predicted and measured values (r = 0.644, p <
0.001). These findings suggest that texture-based machine learning is a feasible approach for neonatal jaundice screening in low-resource settings.