Abstract With the rapid advancement of Artificial Intelligence (AI) technology and its pervasive integration into society, governments worldwide have introduced a range of AI-related policies. In the United States, the use of AI technology has surged significantly since 2021, driven by the emergence of generative AI and its transformative potential. In response, the U.S. Congress has proposed numerous AI-related bills, reflecting growing legislative engagement with AI governance. This study examines 204 AI-related bills introduced during the 117th and 118th Congresses (2021–2024) through computational text analysis, employing topic modeling to identify recurring legislative themes and sentiment analysis to assess congressional attitudes toward AI policies. The findings reveal distinct variations in legislative focus and tone across chambers and political parties, offering a nuanced understanding of how AI-related issues are framed within U.S. policymaking. In addition, the results highlight how AI is connected to broader opportunities and concerns, including national security, technological innovation, and public service delivery. By applying machine learning techniques to legislative texts, this research provides a systematic and scalable approach to understanding AI policymaking. The study contributes to broader discussions on the partisan and institutional dynamics shaping AI legislation in the United States, offering insights into how emerging technologies are shaped by legislative priorities, regulatory attitudes, and broader political contexts.