Exome sequencing (exome-seq) by NGS (Next generation sequencing.) has aided in the finding of a significant number of cancer mutations, however challenges persist in translating oncogenomics data into information that is comprehensible and useful for clinical care. We identified the driver genes and the mutation using the database DriverDBV3, which combines exome-seq data, annotation databases, and bioinformatics methods. This database offered Transforming Growth Factor Beta 1 (TGFB1) and driver genes to visualize the correlations between mutations and driver genes in glioblastoma multiform (GBM). The most aggressive brain cancer is the GBM that affects adults with the lowest life expectancy. This study compiles data illustrating the considerable transcriptional and genomic variability of GBM, focusing on 20 clinically relevant driver genes. With a different profile for driver genes and TGFB1 in GBM, a pattern matched the driver genes' involvement in GBM ontogenesis. Also, we discovered TGFB1 overexpression, which was identified as a driver gene in five different aspects based on the mutation score. Also, we discovered a combination of the six-driver genes EGFR, TP53, PTEN, PIK3CA, PIK3R1, and IDH1 with a unique pattern of differential expression and their distinct distribution of somatic mutation, giving them a significant potential to identify the molecular subtype of GBM. Eight computational techniques were used for the GBM dataset to summarize and calculate the results of the driver genes and TGFB1 implicated in GBM. The differential regulation of these genes concerning distinct cellular pathways for GBM patients were also found in our data. This multi-omics analysis will outline future strategies for applying these molecular markers for patient assessment in regular medical practice.