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Ovarian cancer is the second most common cause of gynecologic cancer death in women around the world. Around 75% of patients present with stage Ⅲ/Ⅳ disease at diagnosis, with five-year survival rates below 45%. Ovarian cancer is the main adnexal malignant mass. Thus, accurate non-invasive risk stratification of adnexal masses is essential for optimal management and outcomes. In recent years, the field of artificial intelligence is developing rapidly. As a branch of artificial intelligence, machine learning could learn efficiently from complex and large amounts of data, which has infinite potential to differentiate benign and malignant adnexal masses. Logistic regression(LR), artificial neural network (ANN), support vector machine (SVM), deep learning convolution neural networks (DCNN) have been widely applied in this field, and achieved good diagnostic performance. This paper will review the history and progress of machine learning in ultrasound diagnosis of benign and malignant adnexal masses.