Accurate estimation of travel time is a crucial challenge in modern navigation systems, where real-time traffic conditions constantly change. Traditional shortest path algorithms, such as Dijkstra’s algorithm, provide optimal solutions under static conditions but fail to account for dynamic congestion, leading to inaccurate estimated times of arrival (ETAs). In this study, we propose a Smart Real-Time Estimation method that incorporates predictive modeling of future traffic congestion to enhance ETA accuracy. Our approach utilizes a traffic simulation framework to evaluate the effects of congestion evolution on route optimization. We conducted simulations on different road networks under varying traffic conditions, comparing our method to the standard Dijkstra algorithm. The results indicate that in medium-traffic scenarios, our predictive model significantly improves ETA precision by anticipating congestion buildup. However, in highly congested environments, while our approach reduces systematic prediction errors, it introduces variability due to the complexity of traffic interactions. Additionally, the presence of traffic signals impacts both methods, with our approach demonstrating better adaptability when congestion patterns are predictable. Overall, our findings suggest that incorporating future traffic estimations into routing algorithms enhances the accuracy and realism of travel time predictions. This method provides a more dynamic and adaptive solution for navigation applications, particularly in environments where traffic conditions fluctuate frequently. By leveraging predictive modeling, our approach offers a viable improvement over conventional shortest path algorithms, making it a promising direction for optimizing real-time route planning.