It is a fact that the aviation industry is expected to develop remarkably in near future. Consequently, delays and environmental problems are caused, due to aircraft congestion in the airports. Thus, it is necessary for the airports to use the existing infrastructure efficiently and generate some changes in the system and the way airports are operated. For this reason, the prediction of aircraft taxi time is substantial in order to help airports understand what is necessary to change so as to optimise their efficiency and reduce the aircraft taxi time. This project concerns Manchester’s airport and data about the aircrafts’ features and external factors were given in order to predict taxi time. This machine learning project was following the CRISP-DM process for data mining. All the processes were handled on Python, as it provides Pandas library which creates a useful data frame that provides an easy way to handle and modify the data. Moreover, the scikit-learn library was used for the machine learning was used for the machine learning procedure, by providing all the algorithm that are necessary for this problem. The machine learning algorithms that were applied are Linear regression, Polynomial Regression, Random Forests and Multilayer Perceptrons. The examination of algorithms that were applied showed that the most suitable for the project is Polynomial regression, because it provides the most precise and accurate prediction of taxi time with accuracy equal to 79.94%. Furthermore, it was noted the importance of each variable as two datasets were applied (one has two extra variables) and the variables were ranked regarding the variable selection technique that was used