The medical industry has become one of the most important fields of research in the current era of innovation, with cancer emerging as a prominent area where effective treatments have not yet been discovered. Early detection of cancer disorders is necessary to improve survival rates. The biggest cause of death worldwide, primarily among women, is breast cancer. Due to their ability to manage complicated, enormous, genomic and chaotic proteomic data sets, soft computing and artificial intelligence give approaches for the early diagnosis of breast cancer tumours. Current models rely their predictions of the conceptual model upon uni-modal data, such as the genetic expression based decisioning. The suggested strategy uses a few attempts to learn forecasting analytics to enhance the current data sets’ ability to predict breast cancer prognosis. The models architecture is primarily responsible for accurate prediction with less error function because of its intelligent architectural layers. Here, the loss function of each individuals is measured using a novel approximated Ensemble Boosting Method (a-EBM), with the goal of lowering the error rate. Compared with Adaboost, SVM, and Random Forest, the F-measure of the proposed model increased by 0.04, 0.037, and 0.01 respectively with the accuracy of 95 ± 1 percentage. Several performance indicators are utilized to evaluate the prediction performance, which shows that the model performs better than the earlier methods.