This project aims to develop an AI-driven Traffic Analysis and Optimization Model to analyze real-time and historical traffic data, predict congestion trends, and suggest solutions for improving traffic flow. The model leverages machine learning to forecast congestion zones 2–4 hours in advance and provides actionable recommendations such as real-time signal optimization, staggered office hours, and infrastructure upgrades. An interactive dashboard visualizes key insights, including traffic heatmaps, congestion trends, and optimized routes. The project is built using Python, Pandas, Scikit-learn, TensorFlow, and visualization tools like Matplotlib and Seaborn. Future enhancements include integrating real-time traffic APIs, improving predictive accuracy with deep learning, and deploying a web-based solution.