Prediction is a statement of an uncertain event which uses past data, analyzes it and predicts the future. This project focuses on predicting whether a flight will be delayed or not on the basis of Logistic Regression. Due to lack of easily accessible appropriate flight data of Nepalese airways, international flight data of United States of America is used as dataset. Historical flight data were used as data sets. The input parameters chosen were Origin, Dest (destination), Unique Carrier, Day of Week, Dep hour (departure hour) and Arr Delay. All the categorical variables were converted into dummy variables. Later those data were split into training and testing sets and fed to the logistic model. The accuracy of the system was found to be 61 percent. As a separate module, this project also focus on providing real time scraped data from TIA website to user and notify them upon change on the flight’s status followed by them.