This research deals with a global issue of neurodegenerative diseases, such as dementia, which can hardly be diagnosed early, since their first symptoms often escape the detection methods of clinical medicine. Recent improvements in machine learning techniques offer improved diagnostic accuracy with advanced image analysis techniques. This paper provides a broad review of the current methods applied for neurodegenerative disease detection with an emphasis on early diagnosis and subtype classification. Using K-means clustering, and watershed segmentation of MRI datasets, we will design a more accurate sophisticated diagnostic tool for disease stage identification. The challenges faced while implementing the tool will be addressed, and future directions will be proposed to achieve further improvement in diagnostic precision and patient outcomes. This work contributes to the current revolution in dementia management through machine learning innovations.