Cervical cancer is a significant global health burden, necessitating a comprehensive understanding of its underlying molecular mechanisms to improve diagnostic and therapeutic strategies. In this study, we conducted an in-depth bioinformatics analysis of cervical cancer using a high-throughput microarray dataset, GSE9750. Through robust screening and selection, we identified 1633 differentially expressed genes (DEGs) associated with cervical cancer. Enrichment analysis revealed crucial pathways and processes, such as DNA replication, cell cycle, and epithelial cell differentiation, implicated in cancer development. Additionally, we discovered key genes, including NEK2, AURKA, FOXM1, CDCA8, and CDC25A, linked to these pathways, which also showed significant differences in expression levels between various clinical characteristics. Our findings shed light on potential molecular targets for therapeutic interventions and contribute to the growing body of knowledge in cervical cancer research. This integrative bioinformatics approach serves as a valuable resource for future studies aiming to unravel the intricate molecular landscape of cervical cancer.