<p>In the era of intelligent cities, IP cameras have advanced beyond simple video recording to real time information processing and analysis. Sequentially processing each video stream is inefficient and limits the ability of multi camera systems to manage large volumes of data effectively. Traditional camera systems are also often limited to specific events within narrow scenarios. To address these issues, we propose a parallel processing architecture for an intelligent real time multi IP camera system. This architecture is designed to efficiently handle the complex and resource-intensive demands of real time multi IP camera processing, utilizing purpose-specific deep learning models and managing CPU computational tasks effectively. The core components include a parallelized camera capture module and a parallelized AI unit, with asynchronous processing between them. The system is optimized to handle real time high definition feeds, enabling efficient vehicle and license plate detection, multi object tracking, traffic violation detection, and license plate recognition. It leverages the latest object detection models, tracking algorithms, and character recognition techniques, and offers scalability through a modular design that allows for the integration of additional deep learning models and decision criteria. The proposed system demonstrated high performance and real time processing in traffic scenarios using frames from 32 real time IP cameras, contributing to more efficient traffic management and automation within smart city infrastructure.</p>