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Cardiovascular toxicity constitutes a major adverse effect associated with cancer therapies. The optimal moment to evaluate and implement strategies for the prevention of cardiovascular toxicity in cancer patients is at the point of cancer diagnosis, preceding the commencement of oncological interventions. This study presents a machine learning pipeline for discovering pre-treatment prognostic biomarkers of asymptomatic cancer-therapy related cardiac dysfunction (CTRCD), using a dataset of 485 female patients diagnosed with breast cancer at the age of 55 or older. We examine a comprehensive set of clinical, biochemical, cardiac imaging markers and integrate them into a feature selection approach based on recursive feature elimination with cross-validation. The results identified a panel of significant features which exhibits an area under the ROC curve (ROC-AUC) equal to 0.75±0.07, including left ventricular ejection fraction, trastuzumab loading dose, body surface area, platelet count, left ventricular posterior wall diameter, white blood cell count, body mass index and interventricular septal diameter in ≥90% of the resampling iterations.Clinical Relevance- Early identification of high-risk patients for CTRCD enables proactive interventions, including the initiation of cardioprotective medications, adjustment of cancer treatments, and enhanced cardiac surveillance. Identifying biomarkers indicative of early cardiac injury allows clinicians to customize strategies aimed at preserving cardiac function and enhancing long-term outcomes for breast cancer patients.