Background: Glioblastoma (GBM) is the most aggressive form of brain cancer, with poor prognosis despite treatments like temozolomide (TMZ). Resistance to TMZ is a significant clinical challenge, and understanding the genes involved is crucial for developing new therapies and prognostic markers. This study aims to identify key genes associated with TMZ resistance in GBM, which could serve as valuable biomarkers for predicting patient outcomes and potential targets for treatment. Objectives: This study aimed to identify genes involved in TMZ resistance in GBM and to assess the value of these genes in GBM treatment and prognosis evaluation. Materials and Methods: Bioinformatics analysis of Gene Expression Omnibus (GEO) datasets (GSE113510 and GSE199689) and The Chinese Glioblastoma Genome Atlas (CGGA) database was performed to identify differentially expressed genes (DEGs) between GBM cell lines with and without TMZ resistance. Subsequently, the key modules associated with GBM patient prognosis were identified by weighted gene coexpression network analysis (WGCNA). Furthermore, hub genes related to TMZ resistance were accurately screened and confirmed using three machine learning algorithms. In addition, immune cell infiltration analysis, TF-miRNA coregulatory network analysis, drug sensitivity prediction, and gene set enrichment analysis (GSEA) were also performed for temozolomide resistance-specific genes. Finally, the expression levels of key genes were validated in our constructed TMZ-resistant cell lines by real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting (WB). Results: Integrated analysis of the GEO and CGGA datasets revealed 769 differentially expressed genes (DEGs), comprising 350 downregulated and 419 upregulated genes, between GBM patients and normal controls. Among these DEGs, three key genes, namely, PITX1, TNFRSF11B, and IGFBP2, exhibited significant differences in expression between groups and were prioritized via machine learning algorithms. The expression levels of these genes were found to be closely related to adverse clinical features and immune cell infiltration levels in GBM patients. These genes were also found to participate in several biological pathways and processes. RT-qPCR and WB confirmed the differential expression of these genes in vitro, indicating that they play vital roles in GBM patients with TMZ resistance. Conclusions: PITX1, TNFRSF11B, and IGFBP2 are key genes associated with the prognosis of GBM patients with TMZ resistance. The differential expression of these genes correlates with adverse outcomes in GBM patients, suggesting that they are valuable biomarkers for predicting patient prognosis and that they could serve as diagnostic biomarkers or treatment targets.