All over the world numerous women get affected with breast cancer, which could be controlled and reduced with early and appropriate intervention. This research endeavors to use ML models for prognostic assessment of breast cancer through the analysis of three distinct datasets: WDBC dataset, SEER dataset, and an additional Breast Cancer Prediction dataset. Each dataset offers a unique perspective on the disease, encompassing a wide range of features from cell nucleus characteristics to patient demographic and clinical data. In this paper, five ML models such as RF, NB, and K-NN have been simulated using three datasets. In WDBC dataset, highest accuracy of 0.982 was achieved by LR. In SEER dataset, random forest achieved the highest accuracy of 0.982 and in breast cancer prediction dataset, LR and NB achieved the highest accuracy of 0.942.