Dental caries generally occurs due to consuming foods containing carbohydrates, such as sucrose, and rarely brushing teeth, thus causing gradual damage to the layers and structures of the teeth. This study aims to build a dental caries level classification system using image processing and machine learning methods. The first step was to analyze and discover the extraction results from Hu’s moment invariants. After successfully extracting the features, the classification was carried out using a Support Vector Machine (SVM) and K- Nearest Neighbors (KNN). This study employed radiographic images of four dental caries classes consisting of Class 1, 2, 3, and 4. A total of 198 images of dental caries were used as training data and 66 images as test data. The classification obtained accuracy value of the SVM and KNN. The highest accuracy was discovered in the Fine Gaussian model of the SVM classification method with 77.6%, while the lowest accuracy was depicted in the Cubic model with 57.4%. Meanwhile, the highest accuracy by using KNN is 100% of accuracy using Fine and Weighted KNN models.