Introduction: Most Indian women are suffering from Breast Cancer. The simple and efficient screening used for Breast Cancer (BC) is Mammograms. Mammogram images are used to detect BC in the early stages. Objective: The main objective of our research is to detect the BC in early stages using Gray Level Co-occurrence Matrix (GLCM) with Machine Learning Algorithms. Methods: Our proposed system is a two-step process which includes feature extraction and classification. Features are extracted from the Mammographic Image Analysis Society (MIAS) database by using a texture-based descriptor called GLCM. These features are passed to classifiers called K-Nearest Neighbor (KNN), Random Forest (RF) and Gradient Boosting by considering 30% as testing data size. Results: The experiments are done as follows: GLCM+RF, GLCM+KNN and GLCM+ Gradient Boosting and the performance of these classifiers are calculated by finding accuracy metric. Conclusion: The conclusion is that GLCM features with KNN classifier give better results than other classifiers.