Objective To evaluate the value of an artificial intelligence (AI)-based real-time ultrasound diagnostic system combined with strain elastography (SE) for the differential diagnosis of benign and malignant breast nodules. Methods A retrospective analysis was conducted on 299 breast nodules from 280 female patients treated at The First Affiliated Hospital of Naval Medical University from Jan. to Oct. 2024. With pathological results as the gold standard, the diagnostic performance of the AI-based real-time ultrasound diagnostic system, SE, and their combination was compared. Statistical analyses were performed using logistic regression, Z-test, and Kappa test. Results The sensitivity (84.85%), positive predictive value (98.82%), negative predictive value (76.74%), and accuracy (89.30%) of the combined diagnosis were all superior to those of the single methods, with a specificity of 98.02%. The area under curve of the combined diagnosis was 0.941, which was significantly higher than those of the AI-based real-time ultrasound diagnostic system (0.863) and SE (0.890) (both P＜0.05), and the combined diagnosis showed the best consistency with pathology (Kappa=0.776). There was a moderate positive correlation between the AI-based real-time ultrasound diagnostic system and SE (r=0.471, P＜0.05). Conclusion The combination of the AI-based real-time ultrasound diagnostic system and SE can significantly improve the diagnostic efficacy for benign and malignant breast nodules. The high specificity and positive predictive value are conducive to reducing overdiagnosis and overtreatment.