The rapid expansion of urban infrastructure and the complexity of analyzing vehicle sudden movements in traffic systems necessitate autonomous traffic monitoring solutions for intelligent, autonomous traffic management. However, traditional methods like manual monitoring and static rule-based detection often fail to meet the real-time requirements of a modern city, resulting in inefficient congestion management, pedestrian safety issues, and inadequate road maintenance. The conventional approaches are highly dependent on human intervention and predefined algorithms, and cannot consequently adapt to dy-namic traffic and the unpredictable movement of pedestrians. With urban populations on the rise, there is an urgent need for Artificial Intelligence-driven solutions that can effec-tively process large volumes of real-time data to ease traffic management and deci-sion-making. This study presents an AI-based traffic monitoring framework that integrates deep learning and natural language processing (NLP) models for improved traffic safety, anomaly detection, and infrastructure optimization. The system comprises high-accuracy object detection with YOLOv8, adaptive pedestrian crossing recognition with Few-Shot Learning (FSL), and contextual analysis and real-time decision-making with LLaMA 3.2B. Through the use of these technologies, along with a BDD100K available dataset, the sys-tem achieves high detection accuracy of 96%, low inference time of 75.1 ms and improved adaptability compared to SOTA of 89%. Results indicate the suitability of AI-driven methods for thoughtful city planning and autonomous mobility, with the potential for AI-driven frameworks to improve urban traffic management by increasing its efficiency and safety.