One of the diabetes mellitus detection methods is to measure the blood glucose by drawing a small amount of blood. Other than that, some non-invasive methods also have been developed, one of the alternative methods is iridology. The mapping of organs that corresponded in iris image can be used to detect damaged tissues of an organ, particularly in the pancreas where insulin hormone is made. This paper focuses on developing a non-invasive diabetes mellitus prediction system through an iris image using image acquisition instrument and image processing algorithm. The processing starts with image enhancement using FFT filter and grayscaling, iris localization using Circular Hough Transform (CHT), and normalization using rubber sheet normalization. Segmentation on the pancreas in iris image then resulted as followed, one ROI of the right eye image and two ROIs of the left-eye image. The image acquisition is done with a maximum of three images taken from 15 healthy subjects and 11 diabetes subjects. Feature extraction method that has been used is the Gabor filter, using the texture feature of the segmented iris image. The confusion matrix is used as an evaluation method to obtain the accuracy parameter of the system. Classification model of Artificial Neural Network (ANN) is implemented to classify between diabetes and healthy subjects with results of accuracy number 91.54% and 89.05% for training and testing data respectively. The result shows that this system can be proposed as a tool to help in medical uses for the prediction of diabetes.