Currently, breast cancer is a significant contributor to mortality among women, ranking second only to lung cancer in terms of cancer-related deaths. Breast cancer arises as a result of the fast proliferation of cells within the breast tissue. Breast cancer can manifest in any region of the breast and can be prevented with early initiation of treatment. Breast cancer is a malignant neoplasm characterized by the uncontrolled proliferation of cells originating from breast tissue. The management of breast cancer is contingent upon the specific subtype of cancer and its corresponding stage, ranging from stage 0 to stage IV. Treatment modalities commonly employed encompass surgical intervention, radiation treatment, and chemotherapy. The objective of this study is to employ various data mining algorithms and technologies to diagnose breast cancer. The identification and analysis of disease patterns in earlier cases have led to the discovery of a novel facet in the field of medical advancement. The dataset used in this study was obtained from the UCI machine learning repository. The research focused on evaluating the classification performance of various algorithms, including Binary Categorization, Pseudo random Forest, Regression Analysis, Multilayer Perceptron, and K-NN, for predicting the occurrence of Breast Cancer Disease. The analysis of the gathered data encompassed an examination of its dependability, true positive rates, F1-score, and Kappa statistics.