Software reuse is a critical practice that helps software developers to increase their productivity. Also, it reduces the developing effort and project budget. Howsoever, some factors may lead to a software reuse failure. Software development companies have to consider these factors to prevent project failure due to software reuse. These factors are not only related to the technical aspects of the project, but they cover the companies’ managerial decisions too. This work incorporates ensemble machine learning to predict successful software reuse experience. To the best of our knowledge, this the first work that used ensemble learning to predict successful software reuse. Also, a feature selection technique was used to extract the essential attributes from the dataset. The empirical study showed remarkable results that scored an accuracy of 100%.