This research presents a new approach for Video Content Analysis (VCA) with a specific focus on detecting and analyzing road traffic congestion patterns. The implemented methodology deploys the YOLOv8 (You Only Look Once version 8) object detection framework, which has been optimized to accurately and efficiently identify road traffic objects within video streams. The primary objective is to enhance the capabilities of traditional VCA systems by capturing and understanding complex road congestion scenarios. This research develops a customized YOLOv8 model optimized specifically for road traffic analysis. The optimization process addresses the challenges inherent in real-world traffic scenarios, including variations in lighting conditions, diverse vehicle types, and dynamic traffic flow patterns. The developed model seeks to enable the robust and real-time detection of congestion-related events, such as heavy congestion and normal congestion, thereby contributing to improved traffic management and overall road safety. Moreover, the study explores the integration of advanced techniques for congestion pattern analysis, including vehicle detection, tracking, and counting. The combination of these features facilitates a comprehensive understanding of traffic dynamics and congestion evolution over time. The proposed approach is trained and evaluated on benchmark datasets and real-world video footage to assess its performance. Experimental results demonstrate the effectiveness of the optimized YOLOv8-based approach in accurately detecting and analyzing road traffic congestion patterns. The system exhibits promising capabilities in handling diverse and challenging scenarios, thereby serving as a valuable tool for intelligent transportation systems, urban planning , clean environment, and traffic management. This research significantly contributes to the advancement of video-based traffic analysis, providing a robust solution for monitoring and mitigating congestion-related challenges on road networks.