Alzheimer’s disease is an advanced varied neurodegenerative illness; it is the foremost reason of dementia in upcoming adult life. Detection of AD is significantly and rapidly growing in investigate field of Medical and Computer Science domain. Early prognosis of AD theatres a vital part in refining handling potentials and upsurges the existence frequency of the patients. Since, detection of AD in early stage is tough task and hence this paper is helpful to find approaches for initial finding of AD through segmentation and quantification of different brain tissues in Magnetic Resonance Imaging (MRI). This paper describes an exploratory architecture for VGG-16 Net CNN Model for Automatic Alzheimer’s illness finding and quantification using MR brain images. The proposed model is attaining the higher accuracy compared with outdated machine learning algorithms.