Accurate and timely knowledge is critical in intelligent transportation system (ITS) as it leads to improved traffic flow management. The knowledge of the past can be useful for the future as traffic patterns normally follow a predictable pattern with respect to time of day, and day of week. In this paper, we systematically evaluated the prediction accuracy and speed of several supervised machine learning algorithms towards congestion identification based on six weeks real-world traffic data from August 1st, 2012 to September 12th, 2012 in the Maryland (MD)/Washington DC, and Virginia (VA) area. Our dataset consists of six months traffic data pattern from July 1, 2012 to December 31, 2012, of which 6 weeks was used as a representative sample for the purposes of this study on our reference roadway -I-270 [2-4]. Our experimental data shows that with respect to classification, classification tree (Ctree) could provide the best prediction accuracy with an accuracy rate of 100% and prediction speed of 0.34 seconds. It is pertinent to note that variations exist respecting prediction accuracy and prediction speed; hence, a tradeoff is often necessary respecting the priority of the applications in question. It is also imperative to note from the outset that, algorithm design and calibration are important factors in determining their effectiveness.