Airplane safety remains one of the crucial areas that must have a robust maintenance strategy due to its impact in the transportation of human beings and goods. Predictive maintenance is a vital means of ensuring complex system such as turbofan engines in airplane are being used safely and optimally. The advent of information and communication technologies provide ways to collect useful data for maintenance strategies and decision making. The acquired data are unstructured and may contained incomplete information. Data mining transform the data to become meaningful and useful for machine learning application. In this paper, data mining techniques for predictive maintenance are presented, with machine learning algorithms applied to predict the maintenance conditions of a turbofan engine and the results are compared. The results show that support vector machine has a slightly better accuracy than the other methods. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.