The objective of this study is to assess the predictive potency of cell senescence-related genes (CSRGs) in breast cancer (BC) and establish a risk signature. Trascriptome data of CSRGs were obtained from the TCGA and GEO databases. Consensus clustering was used to generate CSRGs-based molecular clusters for BC patients. A CSRGs-derived risk signature was built using multiple Cox regression analyses of differentially expressed genes (DEGs) between clusters. The prognosis, immune infiltration, chemotherapy and immunotherapy response between different risk groups were analyzed and compared. Two molecular clusters of BC patients were generated on the basis of 79 differentially expressed CSRGs, which showed distinct prognosis and immune infiltration. A total of 1403 DEGs between the CSRGs-derived clusters were found, and 10 of them were independent prognostic genes that used to construct a risk signature. The results demonstrated that patients with older age and advanced stage presented with a higher risk scores. In addition, the risk signature was found to be associated with outcomes, immune infiltration, chemotherapy and immunotherapy response. Patients in the low-risk group showed a favorable prognosis and higher immunotherapy response than those in the high-risk group. Finally, we developed a highly stable nomogram that incorporates risk signature, chemotherapy, radiotherapy, and stage variables, enabling accurate prediction of the overall survival (OS) of individual patients. To conclude, the signature derived from CSRGs holds great promise as a biomarker for prognostic assessment of BC and may serve as a valuable tool in guiding immunotherapy.