Identifying relationships between breast cancer biomarkers is a prominent topic in bioinformatics and oncological sciences, given their critical role in diagnosis-related research. There is a growing need for automated tools capable of accurately extracting such associations from processed biological samples. Machine learning models can leverage mass spectrometry-derived protein datasets by relating general amino acid composition and physicochemical properties to predict classification labels. This study proposes an ensemble learning framework for the classification of human breast cancer proteins, utilizing labeled biomarker data and ensemble methods. The best-performing model identified through experimentation is a Bagging ensemble consisting of 10 Support Vector Machines (SVMs), each with a regularization parameter C = 3 and kernel parameter γ = 0.06. This model achieved an accuracy of 0.968 ± 0.032, recall of 0.979 ± 0.036, precision of 0.960 ± 0.046, F1-score of 0.968 ± 0.032, and an AUC of 0.992 ± 0.011 under k-fold cross-validation with k = 10. The most influential biomarkers for prediction were indel events involving Proline (P), Lysine (K), and Leucine (L). Additionally, the amino acid pairs Leucine (L)/Glycine (G) and Glutamine (Q)/Methionine (M), along with variations in relative mutability, emerged as principal features contributing to improved classification performance. In conclusion, the Bagging-SVM model demonstrated high accuracy in classifying breast cancer proteins through biomarker identification. Compared to the classifier proposed by [12], which utilized 275 descriptors, this model achieved improvements of 1.23% in AUC and 3.42% in accuracy, along with enhanced precision and recall.