Abstract The recent exponential growth in the data volume and number of identified pulsar stars is due to pulsar candidate search experiments and surveys. In this study, we investigated the existing methods and techniques used for pulsar prediction, such as applying filters based on pulsar observations, which can adversely affect the success of accurate pulsar prediction. Some of the existing methods are not capable of dealing with large volumes of data and others fail to accurately select the best candidates from pulsar observations. Thus, we developed a new approach based on the traditional supervised machine learning algorithm, which yields faster and more accurate results. In this study, we present our hybrid machine learning classifier called the random trees boosting voting classifier (RTB-VC) for predicting pulsar stars. RTB-VC combines tree-based classifiers and it employs the High Time Resolution Universe 2 (HTRU2) data set comprising a set of eight features related to pulsars and non-pulsars. The HTRU2 data set is imbalanced and we solve this problem by using the synthetic minority oversampling technique to generate artificial data and obtain a balanced data set. A feature set is used to separate pulsar and non-pulsar candidates because the different distributions of variables in the data set are helpful for training models. In the proposed approach, the prediction stage of RTB-VC is based on a combination of soft voting, hard voting, and weighted voting to obtain highly accurate and relevant criteria for finally predicting pulsars or non-pulsars. The ensemble-based structure of RTB-VC yields accurate results based on pulsar observations with a high F 1 score for pulsars (98.3%). We evaluated the learning algorithm in terms of its accuracy, precision, recall, and F 1 score.