Nowadays, software is developed from reused components to increase productivity. Predicting the success of reuse helps in assessing component’s reuse potential. In this paper, six machine-learning algorithms were used to predict successful software reuse. Naive Bayes, artificial neural network, K-Star, Hoeffding tree, support vector machine, and k-nearest neighbor were used to evaluate a public dataset. Results showed that machine learning performed very well in predicting software reuse with accuracy reaches 100%.