Diabetes people who also take antibiotics to combat different infections are particularly vulnerable to fungal mouth and tongue infection. The fungus prospers in the saliva of uncontrolled diabetes to high glucose levels. An efficient method for Tongue image classification using Non-Negative Matrix Factorization (NNMF) and various Support Vector Machine (SVM) kernels are presented in this study. The input tongue images are given to NNMF for feature extraction and stored in feature database. Finally, SVM kernels like linear, polynomial, quadratic and Radial Basis Function (RBF) are used for prediction. The system produces the classification accuracy of 92% by using NNMF and different SVM kernels.