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Recent research has demonstrated a strong correlation between the exponential growth of the transportation sector and the increase in urban phenomena such as traffic accidents and congestion. In this context, accurate congestion prediction has become a primary objective to improve urban mobility and mitigate the negative effects associated with traffic.The study proposes a machine learning-based classification model to predict vehicular congestion. Using a dataset that includes variables such as speed, traffic flow, and weather conditions, various classification algorithms were trained and evaluated. The results obtained indicate that models based on Random Forest offer superior performance in the task of congestion prediction.To evaluate the impact of these predictions on urban mobility, the model was integrated into a vehicular ad-hoc network (VANET) simulation environment. The simulation results demonstrated that providing real-time traffic information based on model predictions allows drivers to make more informed decisions, consequently reducing travel times and emissions.