When traffic demand exceeds available network capacity, traffic congestion develops. Lower vehicle speeds, longer journey times, unreliable arrival timings, and lengthiervehicular queueing are all symptoms. Congestion may have a detrimental influence on society bylowering quality of life and increasing pollution, particularly in metropolitan areas. To alleviatetraffic congestion, traffic engineers and scientists require high-quality, comprehensive, andprecise data to forecast traffic flow. The advantages and disadvantages of various data collectingsystems, as well as data attributes such as accuracy, sample frequency, and geographiccoverage, vary. Multisource data fusion improves accuracy and delivers a more complete picture of trafficflow performance on a road network. This study provides a review of the literature on congestionestimation and prediction based on data obtained from numerous sources. An overview of datafusion approaches and congestion indicators that have been employed in the literature to estimatetraffic condition and congestion is provided. The outcomes of various strategies are examined,and a disseminative analysis of the benefits and drawbacks of the methods reviewed is offered.Keywords: traffic congestion; multi source data fusion; traffic state estimation; data collection