Breast cancer (BC) is among the most prevalent malignancies and remains the leading cause of cancer-related mortality in women worldwide. While prior studies have highlighted the associations between insulin resistance (IR) and both tumorigenesis and cancer progression, the prognostic relevance of IR in BC has not been fully elucidated. In this study, we employed a suite of machine learning algorithms and statistical methods to construct a robust prognostic model for BC based on insulin resistance-related genes (IRGs). The model’s prognostic value was subsequently validated in four independent validate cohorts, including METABRIC and three GSE datasets. The resulting IR signature, comprising seven hub IRGs (LIFR, EZR, TBC1D4, NSF, RPL5, SAA1, and PGK1), demonstrated high predictive power for overall survival (OS) across public datasets. Notably, a lower insulin resistance risk score (IRRS) was significantly associated with more favorable clinical outcomes, including enhanced responses to neoadjuvant therapy. Based on single-cell RNA sequencing data, we found that the hub genes were more enriched in T cells, B cells, and epithelial cells. Furthermore, we used machine learning methods to perform feature selection and reduction, which generated a clinically applicable scoring system consisting of the seven hub genes for predicting clinical outcomes in BC patients. This novel IR-based prognostic signature offers a valuable tool for stratifying BC patients by risk and tailoring personalized therapeutic strategies, thus enhancing precision oncology in breast cancer care.