Cystic fibrosis (CF) is a life-threatening autosomal recessive disorder due to mutations in the CFTR gene, resulting in abnormal chloride transport, airway inflammation, and progressive pulmonary damage. While CFTR dysfunction is the main molecular cause, disease severity is modulated by intricate gene interaction networks and inflammatory signaling pathways. In this study, a bioinformatics-based network analysis approach was used to uncover important target genes and pathways for CF. A list of CF-related seed genes was obtained from the DisGeNET database and supplemented by GeneMANIA to build a functional interaction network. Protein-protein interaction analysis was carried out using the STRING tool, and hub genes were selected according to network topology. Pathway enrichment analysis was performed using Reactome.