<p>Alzheimer’s disease (AD) is the leading cause of dementia, affecting millions worldwide and characterized by progressive neuronal loss, brain atrophy, and cognitive decline. Early diagnosis of AD is crucial for improving treatment outcomes and prolonging patients' quality of life, as timely interventions at the earliest stages can slow disease progression and enhance survival rates. However, accurate early detection remains challenging due to overlapping features across disease stages and the complexity of neuroimaging data. Traditional diagnostic approaches and conventional machine learning techniques often struggle to capture subtle variations in early-stage AD, limiting their diagnostic efficacy. To address these limitations, we propose a novel deep learning framework that enhances the diagnostic performance of a pre-trained ResNet-50 model by integrating a Convolutional Block Attention Module (CBAM) and Multi-Head Self-Attention (MHSA). Our framework leverages attention mechanisms to refine feature representations at an early processing stage, enabling precise differentiation between disease stages. The integration of Cross-Attention further improves the model's ability to extract discriminative features from neuroimaging data, optimizing predictive performance. Trained and evaluated on the OASIS Kaggle and MIRIAD datasets, the model achieves state-of-the-art results, including an accuracy and AUC of 99.17% and 99.00% on the OASIS dataset, and an accuracy of 98.59% and AUC of 98.44% on the MIRIAD dataset. Its near-perfect performance across all evaluated metrics establishes its potential as a transformative tool for early AD diagnosis.</p>