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Breast cancer remains a critical global health concern, requiring advanced and reliable diagnostic methods for early detection and effective intervention. This work introduces an integrated ensemble framework that combines multiple dimensionality reduction (DR) techniques, including Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), and Singular Value Decomposition (SVD), with robust machine learning (ML) classifiers for improved breast cancer detection. The publicly available Wisconsin Breast Cancer Dataset (WBCD) was utilized, with rigorous data preprocessing performed to address missing values, anomalies, and class imbalance through stratified sampling and median imputation. To mitigate overfitting and underfitting, dimensionality reduction was coupled with cross-validation and ensemble strategies. The predictive performance of Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Multi-Layer Perceptron (MLP) was systematically evaluated. Experimental results show that SVM consistently achieves a maximum accuracy of 97. 9 % across all applied DR techniques, while MLP and LR also reach 97. 9 % accuracy with PCA and NMF, though MLP exhibits performance variability depending on the selected DR method. The findings provide practical guidance for healthcare practitioners and researchers, supporting the adoption of explainable and scalable AI-driven diagnostic tools. Limitations include the reliance on a single dataset and the need for further validation on larger and more diverse clinical cohorts. Future work will focus on enhancing model interpretability, external validation, and real-world deployment in resource-constrained settings.