Abstract: Breast cancer (BC) is the most prevalent malignant tumor among women worldwide. Accurate assessment of lymph node metastasis status is of great significance for formulating individualized comprehensive treatment strategies, optimizing clinical decision-making, and predicting patient prognosis. As a visual predictive tool based on multivariate regression analysis, a nomogram can integrate multidimensional variables such as clinicopathological features, radiomic parameters, and genomic biomarkers to quantitatively evaluate the individual risk of lymph node metastasis in BC patients. This article systematically reviewed the research progress of nomograms in predicting lymph node metastasis, focusing on nomogram models constructed based on multidimensional data including clinical characteristics, radiomics, and genomics. It evaluated their application characteristics and predictive accuracy indicators, aiming to continuously improve nomograms to build models more suitable for clinical prediction. This article aimed to promote the optimization and standardization of nomograms and provide evidence-based and practical references for the improvement of the precise diagnosis and treatment system for BC.