With more people on the road, congestion is worsening and accidents are becoming more common in cities as a result of the world's fast population rise. This situation highlights the urgent need for efficient traffic monitoring systems. This research aims to develop an advanced real-time traffic monitoring model that utilizes YOLOv8 and OpenCV to improve vehicle detection, identification, and categorization. Tackling this issue is essential for optimizing traffic flow, reducing accident rates, and advancing autonomous driving technologies. Current models often fall short in terms of detection accuracy, processing speed, and adaptability to complex traffic scenarios. To address these shortcomings, the proposed model incorporates YOLOv8 for enhanced real-time object detection, OpenCV for efficient image processing, and machine learning algorithms such as XGBoost and LightGBM to boost classification accuracy. These algorithms facilitate handling large datasets in real time, ensuring robust analysis and adaptability across various urban environments. The findings demonstrate significant improvements in vehicle detection precision and real-time data processing capabilities, directly addressing the challenges of traffic congestion and safety. This model not only provides a scalable and versatile solution for current traffic management but also sets the stage for future advancements in intelligent transportation systems.