Each second, the traffic surveillance system gathers a vast amount of data related to road traffic. Observing these data points manually is a laborious undertaking that also necessitates the use of personnel. Control and monitoring can be done with Deep Learning Convolutional Neural Networks. To create training data, traffic analysis data is first obtained. A transport network is created by transforming the network into a transport application and reintroducing it using self-generated data. This transportation network can explore large areas. Moreover, it is capable of being universally implemented. Moreover, DLCNN is employed to forecast traffic conditions, including but not limited to congested traffic, light traffic, accidents, and fires, based on test samples. In conclusion, the simulations demonstrated that the performance of the proposed DLCNN was superior to that of the existing model.