Every year around 20% of all flights are delayed or canceled. The costs of these events to the airline companies and passengers are in billions of dollars each year. According to a report published in 2010 by UC Berkeley, Federal Aviation Agency (FAA), and National Center of Excellence for Aviation Operations Research (NEXTOR), around half of the total cost (direct and indirect) is paid by the passenger. The goal of this study is to predict airline delays using binary supervised and unsupervised machine learning classification algorithms using the US Domestic Flights data from 2015-2017. We use an expanded set of variables some of which are subject to decisional control of the airlines to help improve the predictions. Different performance measures such as prediction accuracy, recall rates, receiver operation characteristics – area under the curve scores were used to evaluate the efficacy of different algorithms.