With urbanization increasing at a rapid pace, the need for real-time traffic detection is a necessity. Today’s need of an hour is the presence of a good traffic management system. Current Traffic management methods include methods such as the use of infrared sensors and manual counting [3]. While these methods have provided a foundation for traffic management system, they also come with certain limitations such as high installation and maintenance cost and a lack of flexibility in dynamic traffic conditions. This project proposes a real-time traffic detection with deep learning that fills in the gaps of traditional traffic management system by utilizing machine learning techniques. This project uses YOLO (You Only Look Once) algorithm for object detection and, to analyze video footage through camera installed at intersections with accuracy and speed [2]. This model is capable of identifying and classifying vehicles and traffic situations by taking parameters such as time, road width, traffic length into account, and it also supports multi-signal coordination each located at different intersections. The initiative makes use of AI to transform traffic management by improving road safety. The system maintains high precision by ongoing model training, constant updates, and real-time data analysis. The use of neural networks with real-time processing proves to be a robust solution for smart cities and transportation [10].