Automated vehicles, security systems, and manufacturing robots are a few areas that rely heavily on real-time Vehicle detection. The suggested method is significant because it can instantly handle massive amounts of visual input, which is necessary for applications like autonomous navigation systems and traffic monitoring, where quick decision-making is essential. Computational resource constraints, identification accuracy in low-light conditions, and optimizing for diverse Vehicle sizes are some of the issues that still need to be addressed in CNN-based detection systems, despite their advancements. This research proposes an innovative approach called Convolutional Vehicle Detection using Deep Learning (CVD-DL) that aims to achieve accurate, high-speed detection. CVD-DL efficiently detects automobiles with slight latency by utilizing CNNs for comprehensive feature extraction from video streams. Applications where speed and accuracy are of the utmost importance, such as industrial automation, autonomous vehicle navigation, and real-time traffic monitoring, are well-suited to the suggested method. By comparing CVD-DL to more conventional Vehicle detection techniques, the results show that it is far more successful in terms of both speed and accuracy. Improving the ability to respond in real-time while reducing the number of false positives.