Digitalization is exerting an increasingly significant impact on the economic spheres of various countries, placing customs authorities under pressure to adapt to emerging technologies, particularly those directly linked to artificial intelligence (AI). This paper examines the key aspects of implementing artificial intelligence and Big Data in customs operations management. The study explores approaches to applying machine learning and predictive analytics for automating and optimizing customs services processes. It also discusses the deployment of automated data-processing systems for forecasting import and export volumes, detecting smuggling and tax evasion. In addition, the paper investigates the challenges and risks that can appear by the introduction of new information technologies, including cybersecurity, data protection, and international regulatory and legal concerns. The author analyzes ways to improve the conceptual framework for smart customs management aimed at improving the qualitative efficiency of operations carried out by customs authorities, ultimately contributing to the modernization of global trade facilitation systems. Finally, the work proposes a conceptual model of intelligent customs management aimed at enhancing the efficiency of customs authorities and reducing clearance times.