Alzheimer’s disease is a brain disorder that impacts recall, mental skills, and conduct. As the condition advances, brain cells degenerate and die, resulting in the loss of previously stored information. Although there is no cure for this condition, early and effective detection can help slow its progression. An innovative approach based on Iridology, the study of the eye’s iris, enables the analysis of features such as color, texture, nervous rings, and inflammation. By identifying specific patterns in the iris through image processing techniques, this method can aid in detecting Alzheimer’s disease. The disease is influenced by genetic factors, lifestyle choices, and environmental conditions. It is an irreversible condition that gradually damages brain cells responsible for memory. Currently, no definitive methods exist for detecting Alzheimer’s disease. Common symptoms include memory loss, difficulty with thinking, and challenges in writing or speaking (Hernández et al. in Early detection of Alzheimer’s using digital image processing through iridology, an alternative method, IEEE, pp 1–7, 2018 [1]). Iridology, a growing field of alternative research, examines changes in the iris as they relate to various organs in the body. Integrating digital image processing with Iridology offers significant potential for studying neurological disorders, particularly Alzheimer’s. Specialized software analyses iris characteristics such as colour and patterns to identify the presence of the disease. Noise in iris images is minimized using a Gaussian filter, followed by histogram analysis and cropping. The Hough Circle Transform is employed to define the region of interest and transform the circular iris image into a rectangular form. Furthermore, SVM (Support Vector Machine) and CNN (Convolutional Neural Network) classifiers are utilized to detect Alzheimer’s disease (Umesh et al. in Int Res J Eng Technol 03(03), 2016 [2]).