In an effort to alleviate traffic and improve urban mobility, intelligent transportation systems (ITS) relies heavily on forecasting traffic. In the paper, a comprehensive survey on spatial temporal predictive modelling techniques for forecasting traffic has been presented. The focus remains on advanced machine learning and deep learning that have been developed between 2017 and 2025. With the use of state of the art technologies to forecast both in real time scenarios (short-term) traffic prediction and long term forecasting, such as transformer based models, (RNN) recurrent neural networks, convolutional networks on grids and graphs, and (GNN) graph neural networks. Former approaches were examined for strengths and limitation to capture intricate temporal dynamics and spatial interdependencies. Through the above findings, a brand-new conceptual methodology that associates attention mechanisms and graph-based learning to increase prediction accuracy with computing efficiency has been proposed. The performance improvements of newer methods over the conventional methods are also shown through a comparison of the experimental findings.