Alzheimer’s disease (AD) involves region-specific molecular and cellular disruptions across brain tissue. This thesis presents a unified computational pipeline for analyzing both spatial transcriptomics and single-cell RNA sequencing (scRNA-seq) data to dissect AD pathology at tissue and cellular resolution. Using spatially resolved transcriptomes from post-mortem human middle temporal gyrus (MTG) samples (GSE220442) obtained from the NCBI Gene Expression Omnibus (GEO) library, we compared three AD cases to three matched controls, applying Scanpy and Squidpy within the AnnData framework. Unsupervised clustering and differential expression analyses identified five spatially upregulated genes—CHI3L1, C3, SPP1, CRYAB, and QDPR—which localize to glial, inflammatory, and oxidative stress zones in AD tissue. Complementarily, scRNA-seq analysis revealed disease-specific upregulation of genes like HIPK2, ENPP2, BCAS1, MAG, CLDND1, and PPP1R14A, implicating intracellular stress signaling, glial migration, myelin remodeling, and blood–brain barrier dysfunction. Although direct gene overlap was limited, convergent biological themes emerged, including neuroinflammation, DNA damage repair, and lipid-mediated glial activation. By combining spatial and single-cell approaches, we offer a more comprehensive understanding of AD pathogenesis. The integration reveals complementary molecular signatures that inform potential biomarkers and spatially targeted therapeutic strategies. This dual-layered framework highlights the value of multimodal transcriptomics in precision neurodegeneration research and provides a reproducible foundation for future studies.