Dementia is a general term used to indicate any disorder related to human memory. The various memory related problems severely affects the human brain and so the individual feels difficulty in doing their normal physical as well as mental activities. There are different types of dementia exists, but the commonly seen and fatal types of dementia are Alzheimer’s disease (AD) and Parkinson’s disease (PD). In this paper different efficient Machine Learning Techniques are selected analyzed their behaviors in the diagnosis of AD and PD using Positron Emission Tomography (PET). The PET image dataset used in this work consists of 1050 images with AD, PD and Healthy Brain images. The total number of images is split into two different categories for training and testing in the ratio of 7:3. The different machine learning classifiers used are Bagged Ensemble, ID3, Naive Bayes and Multiclass Support Vector Machine. The classification of the AD and PD is carried out by comparing the test image with the trained samples in the database. On comparison of trained samples with the input image for the PET images, bagged ensemble learning classifier worked better than the other classification algorithms and yields an accuracy of 90.3%. © 2020, World Academy of Research in Science and Engineering. All rights reserved.