Purpose/Significance To investigate the role of oxidative stress-related genes in breast cancer prognosis and the tumor immune microenvironment, and to construct a predictive model to aid in prognosis assessment and personalized treatment. Method/Process Based on oxidative stress-related gene sets, an integrated machine learning algorithm is employed to screen for the optimal prognostic model, which is subsequently validated in independent cohorts. A comprehensive predictive model is established by combining survival analysis and multivariate Cox regression. Gene set enrichment analysis (GSEA), single-cell RNA sequencing (scRNA-seq), and tumor microenvironment analysis are utilized to compare pathway enrichment, immune cell infiltration, and immunotherapy response between high-risk and low-risk groups. Result/Conclusion A prognostic model comprising 14 genes is constructed, demonstrating that patients in the high-risk group have a significantly shorter overall survival. A nomogram integrating the risk score and clinical features has excellent predictive performance. The model demonstrates strong predictive capability and potential for clinical translation.