Breast cancer has become one of the causes of mortality of women, which points to a crucial necessity of effective and precise diagnostic methods. This research study provides an in-depth comparison of supervised and unsupervised machine learning on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, which is comprised of 569 patient’s records with 30 diagnostic features. Multiple supervised machine learning models like Logistic Regression, support vector machine, k-Nearest Neighbors, random forest , gradient boosting, and XGBoost were evaluated along with some unsupervised machine learning models including K-means, Gaussian Mixture Model and Hierarchical clustering. Supervised ML models were evaluated using evaluation matrices such as Accuracy, Precision, Recall (Sensitivity), Specificity, F1-score and ROC-AUC, whereas unsupervised ML model were evaluated using metrics such as Silhouette, Calinski Harabasz, and Davies-Bouldin. Results showed superiority of supervised models compared to unsupervised models in predictive accuracy and diagnostic reliability. SVM was the most precise with the highest accuracy of 98.2%, whereas the F1 score and recall was the highest of 97.62% as compared to other Supervised ML models. Conversely, the unsupervised ML models like Hierarchical Clustering and Gaussian Mixture Models had aligned accuracies of about 90% supporting the claim that they could be used in exploring data and grouping the features in the absence of labels. When comparing each model using the analysis of feature ranking, it was found that worst area, mean concavity and worst radius are the most powerful predicting features of the malignancy. This study presented an evidence-based comparison of early breast cancer detection based on supervised and unsupervised ML models, focusing on ways to remain robust when only limited data are available and explainable in terms of their importance.