Alzheimer’s disease (AD), pulmonary arterial hypertension (PAH), and osteoarthritis (OA) are chronic conditions affecting different organ systems and collectively contribute to a substantial burden on long-term patient care. Advances in digital health and bioinformatics enable integrative analysis of large-scale molecular datasets, offering new opportunities to identify shared disease mechanisms relevant to precision and personalized medicine. This study aimed to uncover common differentially expressed genes (DEGs), biological pathways, and regulatory networks across AD, PAH, and OA using an integrative in silico, data-driven framework. Publicly available gene expression datasets were retrieved from the NCBI Gene Expression Omnibus (GEO) and analyzed using standardized computational pipelines to identify DEGs and overlapping molecular signatures. Protein–protein interaction network analysis was applied to identify central hub genes, followed by functional enrichment analysis using Gene Ontology and KEGG pathways. In addition, transcription factor and microRNA regulatory networks were constructed to characterize multi-layered gene regulation. Thirteen shared DEGs were identified among the three diseases, with MMP9, PIK3R1, SERPING1, ORM1, and C2 emerging as key hub genes. Enrichment analyses revealed common involvement in immune regulation, complement activation, extracellular matrix (ECM) organization, and PI3K/AKT signaling, alongside enrichment of immune-related pathways such as PD-1/PD-L1 checkpoint and T cell receptor signaling. Regulatory network analysis highlighted STAT3, NF-κB, and SP1, as well as miRNAs including hsa-miR-21 and hsa-miR-106b, as central regulators of shared molecular processes. Overall, this study demonstrates how integrative bioinformatics and digital health analytics can reveal convergent molecular signatures across clinically distinct diseases, supporting the identification of data-driven biomarkers and potential cross-disease therapeutic targets to enhance patient care. Further experimental validation is required to facilitate clinical translation.