Advanced Machine Learning models with transportation principles lead to develop a more accurate and reliable traffic forecast. Targeted analysis of the most suitable AI architectures and transportation study to correlate flow, speed and density data defined the resulting model. The outcome, based on data-driven techniques, was trained to interpret traffic dynamics and predict the propagation of flows along the network, improving time accuracy and operational efficiency.