The critical identification and prediction of the kind of malignant development should generate an interest in illness research, to assist and manage patients. The criticality of classifying illness patients into high or low risk groups necessitates that several examination groups from the biomedical and bioinformatics fields study and study the use of artificial intelligence (AI) technologies.An approach based on strategic regression and multi-classifiers has been presented to predict breast cancer.To develop deep projections in a different environment based on facts on bosom illness. This article examines the many information mining techniques that make use of classification that may be used to Breast Cancer data to provide deeper projections. Apart from that, this inquiry forecasts the best Model-generating elite by evaluating the dataset using several classifiers. Breast malignant growth dataset was gathered from the UCI AI vault and contains 569 instances with 31 attributes. The data gathering process begins with the simple logistic regression methodology, followed by IBK, K-star, Multi-Layer Perceptron (MLP), Random Forest, Decision table, Decision Trees (DT), PART, Multi-Class Classifiers, and REP Tree. Cross approval with a 10-overlap is used, and preparation is carried out to design and test new Models. The outputs are evaluated against a variety of criteria, including accuracy, root mean square error, sensitivity, specificity, F-Measure, ROC Curve Area, and Kappa measurement, as well as the time required to construct the model. The analysis of the results reveals that, of all the classifiers, Simple Logistic Regression produces the deepest predictions and obtains the best model that produces high and precise results, followed by other techniques. IBK: Nearest Neighbor Classifier, K-Star: Example-Based Classifier, and MLP-Neural Organization Different methods have a lower degree of accuracy when examined using the Logistic relapse methodology.