This study presents an integrated traffic monitoring system for accident detection, vehicle counting by type, and vehicle speed estimation using roadside Closed-Circuit Television (CCTV) footage and machine vision based on the YOLOv11 architecture. The proposed methodology comprises data collection from heterogeneous sources, data preprocessing and augmentation, model fine-tuning on a custom Vehicle–Accident dataset, system deployment through a web-based application, and real-world evaluation. The YOLOv11 models were optimized to detect multiple vehicle categories and clearly defined accident classes under real traffic conditions. Experimental results indicate that the YOLOv11 Large (l) model achieves superior detection performance, with 81.8% precision, 75.8% recall, 82.1% mAP50, and 53.3% mAP50–95. Real-world testing further confirms its effectiveness, yielding an object detection accuracy of 99.24% and low speed estimation errors, with Mean Absolute Percentage Error (MAPE) of 3.56% for video-based evaluation and 5.54% for real-time evaluation. In contrast, the YOLOv11 Nano (n) model offers faster inference and lower computational requirements but exhibits reduced robustness in complex accident scenarios. The trained models are deployed in an interactive web application supporting image, video, and real-time inputs, enabling practical traffic monitoring and decision support. Overall, the YOLOv11l-Vehicle-Accident model is identified as the most suitable configuration for accuracy-critical traffic management systems, while Nano variants are better suited for resource-constrained deployments.