Background: Breast cancer is the most common cancer in women, which has not been completely cured yet. The traditional approaches have low accuracy for breast cancer detection. However, intelligent techniques have been recently used in medical research to distinguish infected individuals from healthy ones, accurately. Objectives: In this study, we aim to develop an ensemble machine learning (ML) method to distinguish tumor samples from healthy samples robustly. Methods: We used an Imperial Competitive Algorithm coupled with a Fuzzy System (ICA-Fuzzy-SR) to identify the most influencing features to recognize tumor samples. To evaluate the proposed method, we used the publicly available Wisconsin Breast Cancer Dataset (WBCD). Results: Benchmarking with the current existing leading methods indicates that our proposed method achieves 95.45% prediction accuracy, which is 3% better than those reported in previous studies. Conclusions: Such results achieve while our model is significantly faster than previously proposed models to solve this problem.