This study explores the potential of leveraging deep learning methodologies, particularly augmented with attention mechanisms, to proactively detect early Alzheimer’s using a multifaceted approach that analyses different data sources, including MRI imagery scans. Attention mechanisms are integrated to enable neural networks to prioritize the most relevant and informative regions or features within the data, thereby enhancing accuracy and elucidating significant disease biomarkers. The objective of this paper is to develop a computational framework for Alzheimer’s risk assessment, aiming to facilitate earlier diagnoses and interventions. Traditional diagnostic approaches often rely on manual feature extraction or simplistic machine learning models, limiting their ability to discern subtle patterns in complex brain imaging data. In response, we propose a novel approach that merges attention mechanisms with convolutional neural networks (CNNs), allowing for automated feature highlighting to enhance interpretability and performance. Through rigorous experimentation, our method showcases significant improvements across crucial metrics, including heightened accuracy, reduced loss and enhanced precision and F1 scores, thereby highlighting its potential as an effective tool for Alzheimer’s disease classification.