Note: This is an unpublished manuscript (submitted for peer review in 2025). AbstractBackground: Axillary lymph node (ALN) status remains a critical factor in staging and treatment planning for early breast cancer. However, reliance on advanced imaging (MRI, CEUS, SWE) limits global access and equity, highlighting the need for low-cost, universally accessible tools.Objective: To develop a simple, reproducible, and clinically applicable nomogram—AURS–Basic—for predicting ALN metastasis using routine B-mode and Doppler ultrasound parameters.Methods: A PRISMA-guided systematic review of 35 studies (2015–2025) was conducted to identify key ultrasonographic and clinicopathological predictors. An evidence-informed synthesis (not a formal meta-analysis) extracted diagnostic accuracy metrics (e.g., odds ratios, sensitivity, specificity), which informed a weighted point-based model stratifying patients into low-, intermediate-, and high-risk groups.Results: The final model incorporates 24 parameters. High-impact predictors include ≥3 abnormal lymph nodes (30 pts), cortical thickness ≥4 mm (25 pts), hilum absence (20 pts), CH (cortex to hilum) ratio >1.85 (20 pts), and short axis ≥7 mm (20 pts). AURS–Basic demonstrates strong predictive accuracy (AUC 0.81–0.85) and performs comparably to more complex models.Conclusion: AURS–Basic is a reliable, low-resource preoperative axillary risk stratification tool. It supports personalised surgical decision-making, may reduce unnecessary SLNB, and helps guide early ALND when needed. The model is freely available as an interactive web calculator, enabling point-of-care implementation using only routine ultrasound.