This paper provides a bibliometric analysis of scholarly output on the application of artificial intelligence (AI) in smart transportation networks (STNs). AI-driven STNs are transforming mobility through intelligent traffic prediction, multimodal logistics, autonomous transport, and infrastructure optimisation. The aim is to systematise existing knowledge, identify dominant research directions, and reveal gaps for future investigation. Based on data from Scopus and Web of Science, the study traces the evolution of research trends, identifies influential publications, and classifies major thematic areas. The integration of AI technologies – such as neural networks, edge computing, blockchain, and federated learning – has significantly advanced traffic prediction, logistics optimisation, and intelligent infrastructure development. The analysis identifies five major research clusters, reflecting focus areas in intelligent mobility, optimisation methods, autonomous vehicles, sustainable urban infrastructure, and big data analytics. The findings highlight the interdisciplinary nature of AI applications in transport and the growing global interest, especially in the last decade. Despite progress, research gaps remain in real-time system integration, multi-agent coordination, and policy adaptation, pointing to future research opportunities in adaptive, secure, and sustainable transport ecosystems.