Abstract: One of the large concerns worldwide is a diabetic disease. This can cause the severe decline of the eyes, including permanent vision loss. Early detection of eye disorders, by effective treatment, improves the danger of survival. The proposed approach is to explore the technique of machine learning to detect diabetic patients using thermographic images of an eye fixed and to include the effect of thermal variation of abnormality within the eye structure as a diagnostic imaging tool that's helpful for clinical diagnosis by ophthalmologists. Thermal images are pre-processed then texture characteristics supported by the grey Level Cooccurrence Matrix (GLCM) from gray images are extracted and categorized employing a classifier with a spread of features. The gray-level co-occurrence matrix (GLCM), also referred to as the gray-level spatial dependency matrix, maybe a statistical procedure for analyzing texture that considers the spatial relationship of pixels. RGB is that the most ordinarily used color space and that we have already addressed it in past tutorials. RGB stands for red, blue, and green. What the RGB model states is that three distinct images actually structure each color image. Blue image, red image, black image. Only one matrix can characterize a traditional grayscale image, but a color image is really made from three. The HSI color model factors each color among three components: hue (H), saturation (S), intensity (I). Various matrices.Keywords: Machine learning, CNN, image processing, Feature extraction