Urban congestion is a major issue for cities around the world, causing detrimental effects on the economy, environment, and quality of life. Faced with the urgency of finding sustainable solutions, this research introduces a revolutionary approach using neural networks for advanced modeling of road user behavior. Our goal is to provide an analytical framework capable of accurately predicting congestion and offering effective management strategies. Through an exhaustive literature review, we identify the gaps in current methods and propose a model that integrates behavioral data with traffic flows to anticipate variations in urban congestion. This study illustrates how a deep understanding of movement dynamics, combined with the power of artificial intelligence, can contribute to smarter and more adaptive transport systems. We also discuss methodological challenges and future perspectives, highlighting the potential impact of our approach on urban planning and sustainable mobility. Ultimately, this research aims to enhance our ability to manage congestion, paving the way towards more fluid, safe, and environmentally friendly cities.