When traffic demand exceeds available network capacity, traffic congestion develops. Lower vehicle speeds, longer journey times, unreliable arrival timings, and lengthier vehicular queueing are all symptoms. Congestion may have a detrimental influence on society by lowering quality of life and increasing pollution, particularly in metropolitan areas. To alleviate traffic congestion, traffic engineers and scientists require high-quality, comprehensive, and precise data to forecast traffic flow. The advantages and disadvantages of various data collecting systems, as well as data attributes such as accuracy, sample frequency, and geographic coverage, vary. Multisource data fusion improves accuracy and delivers a more complete picture of traffic flow performance on a road network. This study provides a review of the literature on congestion estimation and prediction based on data obtained from numerous sources. An overview of data fusion approaches and congestion indicators that have been employed in the literature to estimate traffic 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.