The rapid growth of international trade and e-commerce has significantly increased the circulation of counterfeit products across borders. Counterfeit goods negatively affect national economies, reduce tax revenues, threaten consumer health and safety, and undermine intellectual property rights. Traditional customs inspection methods are often insufficient due to the enormous volume of imported goods and increasingly sophisticated smuggling techniques. Artificial Intelligence (AI) has emerged as a transformative technology capable of enhancing customs risk management, automating inspection processes, and detecting suspicious trade patterns. This study investigates the application of AI technologies by governments to prevent the import of counterfeit products through customs control systems. Using comparative analysis of international practices, literature review, and conceptual modeling, the research demonstrates that AI-driven customs systems significantly improve detection accuracy while reducing inspection time and operational costs. The findings suggest that integrating machine learning, computer vision, big data analytics, and blockchain technologies into customs administration can substantially strengthen national anti-counterfeit strategies.