The COVID-19 pandemic, driven by the SARS-CoV-2 virus, has emerged as an unparalleled global public health crisis. Despite extensive research efforts, the precise etiology of this disease remains enigmatic. In this study, we employ in-silico methods to analyze publicly available gene expression datasets from the Gene Expression Omnibus (GEO), aiming to uncover the underlying molecular mechanisms. Gene expression datasets were retrieved from GEO, and differential gene expression patterns were identified using the GEO2R pipeline. Subsequent analyses included the generation of heatmaps, principal component analysis (PCA), construction of protein-protein interaction networks (PPIs), and KEGG pathway predictions through NetworkAnalyst and DAVID. We also examined key hub genes, microRNA targets, and potential drug targets to elucidate the molecular intricacies involved. Our analysis revealed pivotal hub genes, such as VWF, TNF, E2F1, IL1B, IL10, IL-12A, ITGB-5, ELANE, and PLK1, alongside significant microRNAs like hsa-mir193b-3p, hsa-mir-92a-3p, hsa-mir-16-5p, hsa-mir-1925p, hsa-let-7b-5p, hsa-mir26a-5p, hsa-mir1865p, hsa-mir243a-5p, and hsa-mir-34a-5p. Pathways linked to inflammation, including neutrophil extracellular trap formation, systemic lupus erythematous, complement and coagulation cascades, and the coronavirus disease-COVID-19 pathway, played crucial roles in COVID-19 pathogenesis. Additionally, these identified genes and microRNAs hold promise as potential drug targets and biomarkers. In summary, this research offers valuable insights into the pathways, biomarkers, including gene targets and microRNA targets, as well as potential drug targets associated with the inflammatory and coagulation aspects of COVID-19. These discoveries enhance our understanding of the disease's molecular intricacies and open doors to the development of precisely targeted therapeutic interventions.