Objective <p>Breast cancer (BC) remains the most prevalent malignancy among women. Clinical evidence indicates that genetic variations related to circadian rhythms, as well as the timing of therapeutic interventions, influence the response to radiation therapy and the toxicity of pharmacological treatments in women with BC. This study aimed to identify key circadian rhythm-related genes (CRGs) using bioinformatics and machine learning, and construct a prognostic model to predict clinical outcomes.</p> Methods <p>Transcriptome data for BC were retrieved from The Cancer Genome Atlas database. Univariate Cox regression and least absolute shrinkage and selection operator regression analyses were used to develop a prognostic model based on CRGs. The predictive performance of the risk score model was evaluated. Univariate and multivariate Cox regression analyses were applied to construct the prognostic model and stratify patients into high-risk and low-risk groups. Additionally, differences in immune microenvironment, immunotherapy efficacy, and tumor mutation burden were assessed between risk groups.</p> Results <p>A prognostic risk score model comprising 17 CRGs was developed. The areas under the receiver operating characteristic curve for overall survival at 1, 3, 5, and 7 years exceeded 0.6, indicating acceptable predictive performance. Calibration plots and decision curve analyses demonstrated the use of the model in prognostic prediction. Significant differences in immune microenvironment, immunotherapy efficacy, and tumor mutation burden were identified between the low-risk and high-risk groups.</p> Conclusion <p>The circadian rhythm-based gene model, effectively predicted the prognosis of individuals with BC, highlighting its potential to inform personalized therapeutic strategies and improve patient outcomes.</p>