The paper analyses how AI can analyse data from a variety of sources, including traffic patterns, travel times, weather conditions, and road networks, to help optimize route planning—scheduling and timetabling, and vehicle necessity. The article analyses the theoretical aspects of implementing AI tools for urban logistics processes—public transport in precise, evaluates AI based timetabling modelling, and examines the outcomes of the experiment. The article is based on the methods of scientific literature and analysis and experimental research. Research results have shown that by harnessing the power of AI, transportation professionals can make data-driven decisions faster and more accurately. Although, more extensive and elaborated set of experiments must be made.