Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The rapid expansion of cryptocurrency has revolutionized financial transactions by enabling fast, decentralized, and borderless value transfer. While these innovations have created new economic opportunities, they have also introduced loopholes that can be exploited for illicit purposes, particularly terrorist financing. In the United States, authorities have shown concern about how digital currencies are being used to fund terrorism. Because blockchain transactions are hard to trace and often anonymous especially when tools like mixers and cross-chain swaps are involved, traditional systems struggle to keep up. This study explores the role of Artificial Intelligence (AI) and Machine Learning (ML) in detecting terrorist financing through cryptocurrency within the U.S. context. By employing advanced techniques such as anomaly detection, graph neural networks, and ensemble learning, AI/ML models can analyze transaction patterns at scale, identify hidden relationships, and improve the accuracy of suspicious activity detection. The research integrates insights from blockchain analytics, regulatory frameworks, and compliance practices to assess both technical and institutional factors influencing detection effectiveness.