Triple-negative breast cancer (TNBC) is an aggressive subtype with limited treatment options and a poor prognosis,necessitating accurate early diagnosis to optimize therapeutic interventions. This study aims to develop a predictive method using MRI radiomics and machine learning to distinguish TNBC from other breast cancer subtypes. MRI data from 87 patients with invasive breast cancer were retrospectively analyzed. Manual segmentation of dynamic contrast-enhanced MRI (DCE) was performed, and the segmented masks were propagated to T1- weighted, T2-weighted water, and fat scans. Radiomic features were extracted using PyRadiomics, and feature selection was performed using Spearman’s correlation, mutual information, and least absolute shrinkage and selection operator (LASSO). The EasyEnsemble classifier, an ensemble of AdaBoost learners trained on balanced bootstrap samples, was employed for classification. The combination of DCE and T1W and T2F MRI modalities consistently outperformed individual modalities. LASSO feature selection resulted in the most significant performance improvements, with the highest area under the curve (AUC-score) of 0.93 ± 0.05, balanced accuracy of 0.81 ± 0.04, and F-score of 0.74 ± 0.05. These findings demonstrate the potential of MRI radiomics and machine learning to noninvasively enhance the diagnostic capability of TNBC, thereby contributing to improved patient care and personalized treatment strategies.