Alzheimer’s disease (AD) is a progressive and incurable neurological condition that presents major challenges for early-stage diagnosis. Conventional methods rely heavily on manual interpretation of MRI and PET scans, which can be subjective, time-consuming, and prone to error—often delaying timely intervention. This project addresses these limitations by developing Alz-InsightNet, an explainable, attention-based multimodal deep learning system for early detection of Alzheimer’s disease. The system combines structural MRI and functional PET imaging data to improve diagnostic accuracy and support clinical decision-making. It employs two modified convolutional neural network (CNN) models—ResNet50 and DenseNet201—enhanced with Convolutional Block Attention Modules (CBAM) for MRI analysis, with their outputs integrated through an ensemble approach. For PET image classification, a CBAM-enhanced VGG-19 model is used. To foster clinical trust, the system incorporates multiple Explainable AI (XAI) techniques—Grad-CAM, Integrated Gradients, and LIME—that generate visual interpretations of the model’s predictions. Alz-InsightNet demonstrated strong performance, achieving 98% accuracy with ResNet50, 95% with DenseNet201, 99.63% with the MRI ensemble, and 88% with the PET model. By fusing complementary imaging data and offering interpretable results, this system presents a practical and clinically relevant solution for enhancing the reliability and trustworthiness of early Alzheimer’s disease detection.