Real-time traffic management systems are needed to manage urbanization’s impact on traffic conditions. Traffic dynamics in cities are complex, and traditional signal timing methods and simple vehicle detection cannot handle them. The paper describes a method for improving urban traffic studies using Real-time Dense Analysis and Management using Visual Graph Networks (RDAMVGN) that utilizes deep learning techniques along with Visual Graph Networks based on visualizations. This study aims to develop a robust, dynamic, and accurate approach to traffic density analysis, vehicle classification, and dynamic signal control in order to achieve high accuracy in traffic flow analysis. The proposed RDAMVGN framework incorporates both a LACF-YOLO detection model and a Faster Region-Based Convolutional Neural Network (Faster RCNN) detection model for high-speed and high-accuracy vehicle identification. This framework applies transfer learning to adapt pre-trained features to new traffic environments. This enhances vehicle classification in complex urban scenes and improves the model’s ability to distinguish vehicles from non-vehicle objects. Traffic flow optimization is achieved by using Mask RCNN and LSTM. The comparative analysis includes Fine-Tuned YOLOv8 with Fine-Tuned Faster RCNN, Fine-Tuned YOLOv3 with Fine-Tuned Faster RCNN, YOLOv5 with Faster RCNN, YOLOv3 with Faster RCNN, standalone Faster RCNN, YOLOv5, YOLOv3, Reinforcement Learning (RL), and Deep Reinforcement Learning (DRL) models, evaluate across precision, recall, accuracy, under pre-emption and non-pre-emption scenarios. The RDAMVGN-based detection component exhibits demonstrably superior performance across all evaluation metrics in real-time traffic management systems. It achieves a high precision of 97.75 %, indicating accurate vehicle identification with minimal false positives. Its recall rate of 96.17 % reflects strong detection capability, minimizing missed vehicles. The overall accuracy stands at 98.48 %, indicating robust classification and localization. In pre-emption scenario, the model maintains its lead with pre-emption precision of 94.79 %, pre-emption recall of 98.66 %, and pre-emption accuracy of 97.43 %, showcasing its reliability in real-time traffic prioritization scenarios.