Background <p>Although the clinical outcome of ER + breast cancer patients receiving tamoxifen after surgery is favorable, a proportion of patients experience recurrence or death due to disease progression.</p> Methods <p>In this study, by integrating lipid metabolism gene expression and machine learning data, a prognostic model based on gene expression was developed using the TCGA-ER + BRCA dataset (<i>N</i> = 183) and validated with the GSE17705 (<i>N</i> = 298), GSE22219 (<i>N</i> = 134), GSE42568 (<i>N</i> = 70), and GSE58644 (<i>N</i> = 147) datasets. Patients were stratified into high- and low-risk groups based on the median risk score of the signature. Comparative analyses of survival, genomic features, immune infiltration, and drug sensitivity were performed between these groups.</p> Results <p>Patients in the high-risk group had worse survival outcomes than those in the low-risk group. The five-year overall survival AUC of the model was 0.858, indicating good performance. High-risk patients were characterized by USH2A and KMT2C mutations, genomic amplification, and enriched JAK-STAT pathway and cytokine-cytokine receptor interaction pathways. Resting CD4 + memory T cells, activated mast cells, and myeloid dendritic cells were significantly enriched in the low-risk group, while M0 macrophages were enriched in the high-risk group. Single-cell sequencing analyses also revealed that the model was significantly associated with macrophages and the percentage of proliferating myeloid cells. The signature was also associated with sensitivity to multiple drugs. Cell–cell interaction difference analyses suggested that cancer-related signaling pathways, especially the SIRPα/CD47/IL6 pathway, were decreased in high-risk patients, but these samples exhibited increased SPP1 interactions.</p> Conclusion <p>The signature captures lipid metabolic reprogramming and immunosuppression, providing a biomarker for prognosis and precision therapy in tamoxifen-treated ER + breast cancer.</p>