Summary Regulatory compliance in transportation infrastructure asset management (TIAM) is hindered by aging assets, disjointed oversight, and changing policies. Traditional ways of inspecting and reporting by hand don't work well when you need to do a lot of them. This paper examines recent developments in Artificial Intelligence (AI) aimed at enhancing compliance via automation, real-time analytics, and decision support. We highlight techniques like natural language processing (NLP) for parsing regulatory texts and graph neural networks (GNNs) for modeling asset interdependencies. These are based on peer-reviewed studies on AI applications in transportation that were published between 2015 and 2025. For example, Graph SAGE and other GNNs have shown that they can accurately predict road accidents with less than 22% mean absolute error on traffic datasets [3]. Case studies of U.S. bridge inspections using AI-enabled digital twins show that labor costs can be cut by 20–30% and that each project could save up to $15 million [4, 5]. To deal with interpretability, explainable AI (XAI) methods find a balance between accuracy and openness. However, more complicated models often give up performance for less clarity [8]. Federated learning enables privacy-preserving model training utilizing distributed infrastructure data [9]. Climate simulations based on SSP2-4.5 scenarios show that AI can make the road network more resistant to floods, affecting as much as 13.1% of segments [11]. This work encourages reliable AI for long-term TIAM governance.