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Introduction: This study aimed to elucidate the molecular mechanisms underlying cervical precancerous lesions by employing bioinformatic approaches to identify key genes and signaling pathways. Methods: A comprehensive strategy was adopted, beginning with the analysis of GEO datasets to determine differentially expressed genes (DEGs) between cervical squamous intraepithelial lesions (CSILs) and normal cervical tissues. Protein-protein interaction (PPI) networks were constructed using STRING 11.0 and visualized with Cytoscape 3.7.2. Functional annotation through Gene Ontology (GO) and KEGG pathway enrichment using DAVID revealed biological processes, cellular components, molecular functions, and signaling pathways associated with the DEGs. Gene Set Enrichment Analysis (GSEA) further refined critical genes and enriched pathways. Similarly, quantitative real-time PCR (qRT-PCR) was performed on cervical biopsy samples from eligible patients to validate the bioinformatic predictions. Results: The analysis identified 371 common DEGs across datasets, uncovering 102 biological processes, 33 cellular components, 15 molecular functions, 29 significantly enriched pathways, and three key genes. Clinical correlations demonstrated that lesion severity was associated with age, vaginal microbiota composition, and activation of the ALK gene and PI3K/AKT/NF-κB signaling axis. qRT-PCR confirmed increased Discussion: By pinpointing the molecular drivers of disease, this work provides a foundation for interventions aimed at precisely modulating these pathways, improving clinical outcomes and reducing the overall burden of cervical cancer.