Fuel tankering is a method that is used in the aviation industry to reduce fuel expenses caused by fuel price differences between departure and arrival airport. It provides profitable transport of required fuel for the next flight. Today, there are some basic customizable formulas/models used in the fuel tankering calculation in the literature; however, the customizability of the formulas/models reveals different parameter preferences (such as weather, route, etc.) for the researchers making calculations, and accordingly, the results to be obtained for fuel tankering may vary. Also, an explanatory study in which artificial intelligence, which is used in various fields such as flight planning, diagnose aviation turbulence, is used in fuel tankering estimation/prediction, could not be found in the literature. For these reasons, in this study, it is aimed to predict fuel tankering in the airline industry with machine learning algorithms that learn from raw data independently of these formulas/models. The dataset is obtained from a commercial airline company in Turkey. In this scope, k-Nearest Neighbors Algorithm, C4.5 Decision Tree Algorithm, Naive Bayes Classifier, and Artificial Neural Networks (ANNs) are used to generate prediction models. According to the results of the study, the best performance is obtained with ANNs by using the Backpropagation algorithm (accuracy = 0.838). Furthermore, an online application for predicting fuel tankering is developed with the ANN model. The machine learning model suggested and the online application developed in this study are one of the most important examples of the integration of artificial intelligence to the airline industry in terms of resource allocation and profitable transport. Also, this study will provide a different insight alternatively to the fuel tankering calculations that are used by aviation companies.