Objective By conducting quantitative, co-occurrence, and visual analyses of in domestic and international literature spanning from 2013 to 2023 in the domain of thyroid ultrasound and artificial intelligence research, this study aims to summarize recent advancements in this field and organize the knowledge structure accordingly. Methods CNKI and Web of Science were used as source databases to search related literatures on thyroid ultrasound and artificial intelligence. CiteSpace analysis software was used to draw the atlas of authors, institutions and keywords, and bibliometric analysis was carried out. Results A total of 9515 publications were analyzed, involving 34 institutions and 119 keywords. The knowledge map reveals that the number of domestic publications surpasses international ones overall. Since 2013, there has been a steady increase in both domestic and international publications, with a noticeable acceleration from 2018 onwards, albeit with a recent slowdown in growth. Notably, Jiang Jue, Zhan Weiwei, Luo Yukun, Zhou Qi, Lei Xiaoying, Zhang Bo, among others, contributed significantly to the Chinese literature output, while internationally, authors such as Paul, Saba, and Suri were prolific. A total of 231 Chinese institutions and 226 international institutions published relevant literature. The top three Chinese institutions included the Ultrasound Department of Ruijin Hospital Affiliated with Shanghai Jiaotong University School of Medicine (21 articles), the Ultrasound Medical Department of Peking Union Medical College Hospital (10 articles), and the Ultrasound Laboratory of the Second Affiliated Hospital of Xi""an Jiaotong University (5 articles), along with the Ultrasound Department of Shengjing Hospital Affiliated with China Medical University (5 articles). On the international front, Zhejiang University (9 articles), Shanghai Jiao Tong University (7 articles), Sun Yat Sen University (7 articles), and Huazhong University of Science & Technology (7 articles) were among the top contributors. Inter-institutional collaboration predominantly involved domestic institutions, with Zhejiang University and Shanghai Jiao Tong University playing central roles, while North Eastern Hill University served as the core for international collaboration. Keyword analysis revealed that domestic literature predominantly focused on multi-modal ultrasound for differentiating benign and malignant thyroid nodules, whereas international literature leaned more towards machine learning, deep learning, and artificial intelligence applications. Conclusion CiteSpace visual analysis reveals a growing interest among both domestic and international researchers in the field of thyroid ultrasound artificial intelligence. However, there remains a need for enhanced multi-center collaboration across institutions, teams, and regions to facilitate deeper and more comprehensive research endeavors.