Spatiotemporal neural networks (STNNs) in urban traffic flow prediction systems thus emerge as an integral part of the evolution toward city traffic management and optimization. Traditional models, typically rooted in more simplistic statistical techniques, are incompetent in precisely modeling complicated spatial-temporal patterns in urban traffic, hence leading to suboptimal predictions and further congestion. This paper proposes a new STNN approach that makes use of convolutional layers for spatial feature extraction and LSTM/GRU layers for temporal sequence modeling to deeply understand traffic dynamics. The model was trained with rich datasets covering all traffic scenarios and environmental conditions, thereby enhancing its generalizability across different urban contexts. Quantitative analysis indicates that STNNs reduce prediction errors of traditional methods by 25%, significantly improving the accuracy and reliability of traffic forecasts. This is very critical in urban settings, where accurate traffic predictions can result in finer traffic management strategies—such as optimized signal timings and route planning which may further reduce congestion by up to 20% in simulated scenarios. In a nutshell, the STNN applied in traffic prediction does not only make sure that there is substantial improvement in the accuracy of forecasting but also sets its base toward more intelligent and efficient urban traffic management, thus supporting smarter and more sustainable cities.