The increasing breast cancer cases and high mortality rates have driven researchers to develop efficient, reliable, and scalable computer-aided diagnosis (CAD) systems. Unlike traditional assessments prone to human error, CAD It is vital to identify the recurrence of the breast cancer to create a clear schema of therapy and as well personalized medicine. In order to perform this, the data needs to be structured from the unstructured form, which is difficult to process and then combine it with existing medical information for clear diagnosis. A useful technique for predicting the recurrence of breast cancer is the application of sophisticated ML alias machine learning algorithms. The methods were developed and assessed using histological observations of breast cancer pictures. The data processing capability will be used to create spotting of the variant of cancer based on the current understanding of medical data. We will use the retrieved data from the data processing system to identify the probability of breast cancer using the sophisticated, updated machine learning algorithm. The best course of action for recurrent breast cancer will be determined and appropriately applied with the help of this prediction procedure. We used trained and test datasets of breast cancer to assess the prediction of five algorithms: logistic regression, Adams algorithms, decision trees, convolutional neural networks and random forest classifiers. Out of the five algorithms, the Random Forest classifier was shown effective circa 99.74% accuracy rate in diagnosing breast cancer recurrence. We are optimistic that this study will open the door to future accurate breast cancer recurrence diagnosis.