 Alzheimer's disease, AD, is one of the most common neurodegenerative diseases affecting millions of people worldwide. Until now, there has been no effective treatment to cure it, but there are drugs to slow its progression. Early detection of AD is therefore essential to take preventive measures before it progresses to irreversible brain damage. Magnetic resonance imaging, MRI, techniques have contributed to the diagnosis and prediction of its progression. However, MRI images require highly skilled doctors and radiologists, and the analysis of MRI images takes time to analyze each slice. Deep learning techniques play a vital role in analyzing a large number of MRI images with high accuracy to detect AD and predict its progression. Because of the similarities in the characteristics of the early stages of AD, this study aimed to extract the features in several methods and integrate the features extracted from more than one method into the same features matrix. Three methodologies were developed, each with two systems, with all systems aimed at achieving satisfactory accuracy for the detection of AD and predicting the stages of its progression. The first methodology is by Feedforward Neural Networks, FFNN, with This article was authored by Ahmed Khalid, Ebrahim Mohamed Senan, Khalil Al-Wajih, and others. We are article.tv, links in the description below.