This case study focuses on the use of deep learning methodologies for optimizing traffic management in Ludhiana with an emphasis on improvement in real-time traffic flow and prediction accuracy. We used simulated traffic data with vehicle counts, average speeds, and traffic conditions like Light, Moderate, and Heavy, to train a neural network model that predicts traffic conditions based on these features. The test accuracy was around 85%, and validation accuracy peaked at 82%. The confusion matrix gave a good classification with approximately 90% accuracy in the prediction of ‘Heavy’ traffic. Also, the value of the AUC of the ROC curve was high; therefore, the model performed well in distinguishing between traffic conditions. Further analysis of feature importance has returned the top two features as being vehicle count and average speed. It provides a balanced representation of the traffic conditions, which is important for training the model. The scatter plot of predicted vs. actual conditions along with the analysis of the prediction errors offered insight into the performance of the model and highlighted the areas that needed further refinement. These results provide insight into what deep learning can bring toward fine-tuning traffic management strategies and creating a track toward a more responsive and efficient urban traffic system.