GeneFormer, an advanced deep learning model, has been utilized to classify single-cell RNA sequencing (scRNA-seq) data in Alzheimer's disease (AD), offering insights into the disease's molecular complexity. AD, marked by cognitive decline and neuropathological features such as amyloid-β plaques and neurofibrillary tangles, remains poorly understood on a cellular level. scRNA-seq provides a high-resolution view of cellular heterogeneity, crucial for unraveling the molecular underpinnings of AD. This study applies GeneFormer to scRNA-seq data from AD patients to identify distinct cellular subpopulations and their gene expression patterns. By leveraging the model's capacity to handle large-scale, high-dimensional genomic data, it effectively classifies cells into relevant subtypes, highlighting differences in gene expression that may contribute to AD pathology. The approach enables a more nuanced understanding of the cellular landscape in AD, identifying potential targets for therapeutic intervention and shedding light on the disease's progression mechanisms.