In the cutting edge world, aircraft assume an essential job for transporting individuals and products on time. Any deferral in the timings of these flights can unfavorably influence the work, what's more, the business of thousands of individuals at any given minute. Estimating these postponements is essential amid the arranging procedure in business aircraft. A few procedures have just been proposed for outlining models to gauge the deferral in takeoff time of air ship. In our paper, a two-stage predictive model was developed employing supervised machine learning algorithms for the prediction of flights. The first stage of the model performs binary classification to predict the occurrence of flight delays and the second stage does the regression to predict the value of the delay in minutes. Then, each of the algorithms’ prediction accuracy and the receiver operating characteristic (ROC) curve were compared. Furthermore, an application was fabricated utilizing the model which uses highlights that are promptly accessible before the takeoff of a plane furthermore, can educate travelers and aircraft about flight delays in advance, To reduce the monetary losses.