Road accidents often result from drivers overlooking vehicles with open doors, particularly on high-speed expressways, where limited reaction time heightens the risk of collision. Traditional solutions involve promptly warning drivers to close open doors when approaching vehicles are detected. However, challenges arise with RADAR systems inaccurately distinguishing between open and closed doors due to signal limitations. This paper introduces a Sudden Door Opening Warning System (SDOWS) utilizing a Front Camera system and Deep Learning. To address the absence of a publicly accessible dataset, a synthetic dataset was created using Unity3D, simulating scenarios with open vehicle doors for training. A comparative study is conducted on object detection models, including YOLOv4 and YOLOv7, to detect vehicles within the path. Subsequently, a CNN classifier is employed to assess their door status, distinguishing between open and closed states. A tracking algorithm is integrated to enhance detection accuracy by continuously tracking individual vehicles. The proposed method attained maximum accuracy of 94.4% with a mAP of 0.5 for vehicle detection and 96.6% accuracy in classifying the door status.