The most common cancer among women is breast cancer. Around 12% of women are affected by it all over the world. Recurrent breast cancer is a term used for breast cancer which returns even after a successful treatment. This research aims to use Machine learning to detect and predict the recurrence of breast cancer; and compare all the models by using different metrics like accuracy, precision, etc. The models built can help predict the recurrence of breast cancer effectively. All the models are built using the Wisconsin Prognostic Breast Cancer Dataset(WPBC). The models built are Multiple Linear Regression, Support Vector Machine, which was build by using RBF Kernel and Leave-One-Out(K-fold Cross-Validation) and Decision Tree using metrics like Gini Index, Entropy and Information Gain. Support Vector Machine and K-fold Cross-Validation gave the best results for recurrence and non-recurrence predictions