Abstract

The increasing complexity of global trade and cross-border movements necessitates advanced solutions for maintaining border security while ensuring efficient cargo flow. This study proposes a strategic framework for enhancing cargo screening and intelligent border security through the integration of automated detection technologies. Emphasizing a multidisciplinary approach, the framework incorporates artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), and advanced imaging systems to improve threat detection accuracy, operational efficiency, and situational awareness. The research highlights key challenges including false positives, data interoperability, and the need for real-time analytics, and proposes solutions involving adaptive algorithms, data fusion techniques, and centralized command platforms. Furthermore, the framework supports risk-based screening, enabling prioritization of high-risk cargo and reducing bottlenecks at ports of entry. Through case studies and technology assessments, the framework demonstrates potential for reducing manual inspections, enhancing decision-making capabilities, and fostering international collaboration on border security standards. The findings contribute to the discourse on smart border management by offering actionable insights for policymakers, security agencies, and logistics stakeholders aiming to modernize border control infrastructures in line with emerging threats and global security demands.