Dooring accidents occur when a vehicle door is opened into the path of an approaching cyclist, motorcyclist, or other road user, often causing serious collisions and injuries. These incidents are a major road safety concern, particularly in densely populated urban areas where heavy traffic, narrow roads, and inattentive behavior increase the likelihood of such events. To address this challenge, this project presents an intelligent computer vision based warning system designed to detect approaching vehicles and alert occupants before they open a door. The system can operate using either the existing rear parking camera in a vehicle or a USB webcam in vehicles without such a feature. The captured live video stream is processed by a Raspberry Pi 4 microprocessor, chosen for its compact size, low power consumption, and ability to support machine learning frameworks. The video feed is analyzed in real time using MobileNetSSD, a lightweight deep learning object detection model optimized through TensorFlow Lite to ensure smooth and efficient processing even on resource- constrained hardware. Detected objects are classified, and the relative distance of approaching vehicles is estimated based on bounding box dimensions and simple geometric calculations. If a vehicle is detected within a predefined safety distance, the system immediately displays a clear visual warning on an in-vehicle screen, giving occupants enough time to delay opening the door and avoid a potential collision. The system was successfully implemented and tested on both a laptop and Raspberry Pi, demonstrating high accuracy, low latency, and minimal hardware requirements, making it cost effective and scalable. Looking forward, the design allows for future upgrades such as automatic door locking when a hazard is detected, audio and haptic alerts for greater situational awareness, integration with other vehicle sensors for improved detection accuracy, and seamless incorporation into commercial advanced driver assistance systems, providing a practical, affordable, and effective solution to enhance road safety and protect vulnerable road users.