The increasing complexity of global trade necessitates efficient customs operations to facilitate seamless cross-border transactions. This study aims to investigates the effect of AI-driven customs processes on trade facilitation efficiency in Jordan. A quantitative research approach was used. The data were collected using a structured survey questionnaire, which measured respondents’ perceptions of AI-driven customs processes and their impact on trade efficiency. The study targeted 112 professionals working in Jordanian customs and trade facilitation sectors. PLS-SEM, Smart PLS-4 was used to analyze the data. The findings confirm a strong positive relationship (path coefficient = 0.957, p = 0.000) between AI-driven customs operations and trade facilitation efficiency. The paper make several important contributions to academic literature and trade policy, this study highlights the urgent need for investment in AI technologies to modernize customs operations in Jordan. The results demonstrate that AI-driven risk assessment models, automated documentation systems, and real-time data analytics contribute to significant efficiency gains in customs procedures. This benchmarking insight enables Jordan to leverage successful AI frameworks implemented in international trade facilitation systems and adapt them to its specific regulatory and economic environment. While this study demonstrates AI’s positive impact on trade efficiency, it is crucial to explore potential risks, including cybersecurity threats, algorithmic biases, and data privacy concerns Future research should examine how Jordan can address these challenges to ensure a secure, transparent, and efficient AI-driven customs system.