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This research is justified from the scientific field since through the development of an intelligent system will support mammographic analysis in timely detection of breast tumors thus generating an innovative idea that contributes to the science of health. From the financial field it is justified that this system is not very expensive compared to other systems that are in the technological market, it will also reduce the waiting time for a timely and early diagnosis of breast cancer, thus obtaining more patients attended that will bring high prestige to the institution. Continuing with the social sphere, it is justified since it intends to support the mammographic analysis, where there will be a minimum margin of error and in addition to helping the oncology area by having a timely diagnosis. Also this system indirectly benefit the students of the medical career, as well as the specialists in charge of this area. Finally, this present investigation is justified technologically since present technologies will be used as much as software and hardware that will allow this intelligent system to be carried out in order to support the radiologist with the mammographic analysis. However, this research on breast cancer, which successfully carried out the calculation analysis, using various models of Machine Learning like logistic regression, support vector machine, decision tree, and random forest Consequently, Logistic Regression with dependent and independent variables results in the test data accuracy value of 96%. Thereafter, SVM vide linear hyperplane determining the affected patients with breast cancer and number of predictions achieved 97% of accuracy. Subsequently, the Decision Tree modeling with Random Forest achieved 96% of accuracy collectively.