Type 2 Diabetes (T2D) is a complex metabolic disorder characterized by coordinated dysregulation of multiple genes and biological pathways. Although transcriptomic technologie senable large-scale analysis of gene expression changes in T2D, many studies rely primarily on differential gene expression analysis, which often fails to capture gene–gene interactions and systems-level regulatory mechanisms. To address this limitation, this study applied a transcriptomics-based gene interaction network approach to investigate the molecular architecture of T2D. Publicly available RNA-sequencing data from T2D patients and healthy controls were analyzed using established bioinformatics pipelines. After quality control and normalization, differential expression analysis was performed, followed by the construction of gene co-expression networks and identify functionally related gene modules. Disease associated modules were further examined to detect highly connected hub genes based on network topology and connectivity measures. Functional enrichment analysis of hub genes using Gene Set Enrichment Analysis (GSEA) was not feasible, as the available data did not meet the input requirements of the method; therefore, pathway-level enrichment was performed using GSEA on a ranked gene list derived from differential expression statistics. Network robustness was assessed through connectivity strength and network density analyses to ensure reliability. The analysis identified distinct gene modules and central hub genes associated with metabolic regulation, mitochondrial function, and cellular homeostasis relevant to T2D. Overall, this study demonstrated that network-based transcriptomic analysis provides deeper biological insights than traditional gene-centric approaches and offers a systems-level framework for understanding the molecular mechanisms underlying T2D.