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The largest disruptive component of traffic management system is road transport. Intelligent Transportation Systems (ITS) necessitate exact traffic forecast. However, transport departments continue to struggle to choose an ITS prediction method. A user must be able to appropriately utilize estimation model information. This paper covers contemporary estimation approaches, explains their fundamental concepts and evaluates different traffic estimation approaches for the benefit of decision makers. Each method is classified as Machine Learning (ML), Artificial Intelligence (AI), Deep Learning (DL) or hybrid algorithms. Many surveys are model or data-driven. This study is the first to examine traffic prediction using a variety of algorithmic and methodological approaches to key traffic parameters. Predictive evaluation of dependent variables is done and each algorithms traffic characteristics are examined in chronological …