Urban traffic congestion is a challenging problem that arises because our cities grow and the speeding number of vehicles makes it inevitable to have efficient traffic flow prediction models. The classic machine learning methods, as well as the popular ARIMA model, have difficulties in targeting and capturing the complexity and irregularity of urban traffic. The focal point of cities currently is to counter the problems with traffic which have been existing for a long time. To solve this, deep learning models like RNN, LSTM, GRU, and Transformer-based systems are being used for their ability to model sequential dependencies. Another study that analyses and compares these models for traffic flow prediction using the METR-LA dataset where the data comes from 207 sensors and are repeated in various cycles was carried out. The findings of the experiments revealed that GRU was the best of all the models and had a lower RMSE (10.3398) and a strong R2 score (0.6369) as well. It implies that the model learns traffic patterns well. Followed by LSTM with RMSE (10.4586) and R2 (0.6394), RNN performed decently. The Transformer model which did not perform up to the mark with RMSE (11.6764) and R2 (0.5392) were the two key components that hinted the limitations of the model in this case in the urban traffic complexities. Further research should deal with hybrid systems and data fusion techniques to improve the accuracy of the prediction process, making ITS (Intelligent Transportation Systems) more efficient in managing congestion and optimizing urban mobility.