Abstract: The mortality rate coming from breast cancer (BC) is far greater than that of any other kind of cancer. The use of prediction models that are based on machine learning (ML) holds the possibility of early detection procedures for breast cancer. However, conducting an examination of models that can accurately identify cancer remains difficult. In this study, we constructed five alternative prediction models to increase the accuracy of breast cancer diagnostics. These models were based on Data Exploratory Techniques, which we referred to as DET. Before models were developed, four-layered essential DET, such as feature distribution, correlation, removal, and hyperparameter optimization, were thoroughly investigated in order to locate the most accurate feature categorization of malignant and benign classes. The Wisconsin Diagnostic Breast Cancer (WDBC) was modified to include these suggested methods and classifiers. In order to evaluate the effectiveness of each classifier and the amount of time it takes to train it, standard performance measures such as confusion matrices and K-fold cross-validation approaches were used. The diagnostic ability of the models increased as a result of our DET; specifically, the Support Vector Machine (SVM) gained 97.6%, the Logistic Regression (LR) with 97.07%, the Extreme Gradient Boosting with 97.07%, the Gradient Boosting Classifier with 96.4% and the Voting Classifier (VC) with 98.2% accuracy using the WDBC dataset. In addition, we examined the correctness of our important findings in relation to those of earlier investigations. The method of implementation and the results may direct medical professionals towards the adoption of an efficient model that provides a practical comprehension of breast cancer tumours as well as a prognosis for the disease.