Medical image analysis plays an important role in early breast cancer detection through mammographic image analysis. But, some information in the GLCM based Haralick texture features is redundant and irrelevant, which can impact performance in classification. In this study, a feature selection framework based on Cuckoo Search Optimization (CSO) is presented to select the optimum Haralick features for the breast cancer classification. The proposed approach consists of the preprocessing of mammograms, extraction of features from the GLCM, optimization of the GLCM features using the CSO technique, and classification using machine learning algorithms. Fourteen extracted GLCM attributes were used in the experiments performed on MIAS. The CSO-KNN model obtained the best classification rate of 90.00% with 6 selected features, which means that 8 attributes have been removed. The proposed framework significantly reduces feature dimensionality and improves the classification efficiency in computer-aided breast cancer diagnosis.