Alzheimer's disease (AD) is a kind of dementia characterized by memory loss in early stages. Damage to the nerve in the brain responsible for learning, reasoning, and remembering (a cognitive function) causes these symptoms. Treatment and patient care are most successful when they follow a timely and exact diagnosis. In this research, a Multi-Layered Dynamic- Feed Forward Neural Network (MLD-FFNN) method is used to analyse Magnetic resonance imaging (MRI) images to make accurate AD predictions. The approach has three essential components: image preprocessing, image segmentation, and classification. MRI datasets are used in the study to categorize subjects as non-demented, very mildly demented, mildly demented, or moderately demented. The initial step, data preprocessing, improves the quality of MRI images. Histogram equalization was designated as one of the methods discussed. Images' contrast may be improved with the use of technique called histogram equalization. Segmentation of images follows after preprocessing. Here, MRI images must be segmented into informative sections. The Gaussian filter (GF) method is used for image segmentation. When smoothing or filtering images, a method is used. Finally, a MLD-FFNN method is utilized for categorization. The segmented MRI scans are presumably the input for this neural network, which is trained to predict whether or not a patient has AD. This research shows effectiveness for early detection and treatment of AD because of its potential for precise prediction. Based on experimental results, this investigation achieved higher accuracy than other methods. The automated AD classification has the potential to serve as a useful aid for medical professionals in making diagnoses of AD and determining the best course of therapy.