Advancements in data science and technology have fueled the exploration of intelligent systems for early breast cancer detection. This study presents a comprehensive framework spanning data preparation, preprocessing, model selection, evaluation, and interpretation. Top-performing models, employing KNN for WBCD and ANN for WDBC, achieve impressive accuracy (97.7% and 98.6%, respectively) and precision rates (98.2% and 94.4%, respectively). The integration of Explainable Artificial Intelligence (XAI) techniques enhances interpretability, providing insights into model decisions. Permutation importance underscores the significance of the Bare Nuclei feature in malignant breast cancer detection. Insights from Partial Dependence Plots (PDP) deepen the understanding of tissue architecture changes and breast cancer growth. LIME plots visually correlate elevated "Concave points worst" values with malignant predictions. Shapley values identify Bare Nuclei as a major contributor, revealing a potential indirect relationship between "bare nuclei" and "area worst." This nuanced interplay between features offers valuable insights into breast cancer classification. In conclusion, this study introduces an effective framework emphasizing both accuracy and interpretability in breast cancer detection. The findings highlight the pivotal role of specific features and lay the groundwork for future investigations into the underlying biological mechanisms of breast cancer onset.