In the modern world, airlines play a vital role for transporting people and goods on time. Any delay in the timings of these flights can adversely affect the work and business of thousands of people at any given moment. Forecasting these delays is very important during the planning process in commercial airlines. Several techniques have already been proposed for designing models to forecast the delay in departure time of aircraft. But because of the continuously increasing complexity of the airplane transportation and the amount of data related to it, designing accurate prediction methods has become very difficult. In this paper we utilise the method of logistic regression which is a supervised learning method to predict delay in departure times of aircraft. We utilise the Microsoft Azure Learning Studio platform which is an Integrated Development Environment for utilizing machine learning for training and testing the model on the cloud. We also join weather data such as temperature, humidity, precipitation, dew point along with the airport data to derive more accurate predictions as well as find out the effect of weather changes in flight delays. Our method was able to achieve about 80 percent accuracy in predicting whether a given aircraft would be delayed or not based on the training using past data. © 2017 IEEE.