Real-time pedestrian trajectory prediction is essential for enhancing safety and urban mobility, particularly in dense and dynamic environments. This paper introduces a video data processing system that accurately predicts pedestrian movement by analyzing sequences of video frames in real time. The system effectively handles challenges such as overlapping individuals, partial occlusions, and diverse walking behaviors, making it suitable for real-world deployment. The architecture is designed to be both modular and scalable, allowing for seamless integration into various applications such as traffic management, urban planning, and pedestrian safety enhancement. A user-friendly interface provides real-time visualization of the predicted trajectories, enabling accessibility for both technical and non-technical stakeholders, including urban planners and public safety officials. Extensive experiments conducted on multiple diverse datasets demonstrate the system’s reliability and accuracy across various conditions, including crowded scenes and irregular pedestrian movement. The system successfully captures complex behavior patterns and provides predictive insights that can help reduce pedestrian-related accidents. This research contributes significantly to the field of intelligent pedestrian monitoring systems. By combining real-time responsiveness with accurate trajectory prediction, the proposed system supports the development of smarter and safer urban infrastructure, fostering proactive decision-making and improved pedestrian safety in modern cities.