This study presents a comprehensive analysis of Alzheimer's disease (AD) classification using deep learning techniques, particularly focusing on early detection of cognitive aging and AD through neuroimaging and transfer learning. The research uses images from the Alzheimer's Dataset (4 classes) available on Kaggle, which includes non-dementia (NONDEM), very mild dementia (VERDEM), mild dementia (MILDEM), and moderate dementia (MODDEM). Six pre-trained neural networks—VGG-19, VGG-16, ResNet-50, InceptionV3, Xception, and DenseNet169—are evaluated for their classification performance. The study finds that VGG-19 and VGG-16 achieve the highest overall precision (92.86% and 92.83%, respectively) in detecting AD. The study highlights the effectiveness of transfer learning in overcoming the challenges of limited data and complex networks. It also discusses the role of medical imaging techniques such as MRI, DTI, and PET in AD diagnosis. The research emphasizes the importance of early detection and treatment to slow the progression of AD. Convolutional neural networks (CNNs) are noted for their high performance in AD classification, although they require large datasets and significant computational resources. Recent advancements show that CNNs can be adapted to handle these challenges. The study also references previous research that uses cross-modal transfer learning and deep learning-based segmentation to improve AD classification. Overall, the study demonstrates the potential of deep learning in accurately and efficiently classifying AD stages using neuroimaging data.This study presents a comprehensive analysis of Alzheimer's disease (AD) classification using deep learning techniques, particularly focusing on early detection of cognitive aging and AD through neuroimaging and transfer learning. The research uses images from the Alzheimer's Dataset (4 classes) available on Kaggle, which includes non-dementia (NONDEM), very mild dementia (VERDEM), mild dementia (MILDEM), and moderate dementia (MODDEM). Six pre-trained neural networks—VGG-19, VGG-16, ResNet-50, InceptionV3, Xception, and DenseNet169—are evaluated for their classification performance. The study finds that VGG-19 and VGG-16 achieve the highest overall precision (92.86% and 92.83%, respectively) in detecting AD. The study highlights the effectiveness of transfer learning in overcoming the challenges of limited data and complex networks. It also discusses the role of medical imaging techniques such as MRI, DTI, and PET in AD diagnosis. The research emphasizes the importance of early detection and treatment to slow the progression of AD. Convolutional neural networks (CNNs) are noted for their high performance in AD classification, although they require large datasets and significant computational resources. Recent advancements show that CNNs can be adapted to handle these challenges. The study also references previous research that uses cross-modal transfer learning and deep learning-based segmentation to improve AD classification. Overall, the study demonstrates the potential of deep learning in accurately and efficiently classifying AD stages using neuroimaging data.