Narrative review discusses AI and machine learning in cardio-oncology to mitigate cancer therapy-related cardiovascular toxicity risks

Cancer therapy-related cardiovascular toxicity (CTR-CVT) threatens the sustainability of oncological advancements, demanding innovative approaches for early risk stratification. This review synthesizes how artificial intelligence (AI) is redefining cardio-oncology through multimodal integration of multi-omics, dynamic imaging, and real-world biosensor data. By decoding novel pathophysiological mechanisms and enabling continuous risk reclassification, AI transcends traditional static paradigms to generate patient-specific toxicity trajectories. Crucially, AI-driven interventions shift clinical practice from reactive monitoring to preemptive cardioprotection. While challenges in data heterogeneity, model interpretability, and equitable implementation persist, emerging solutions like federated learning and explainable AI pave the way for robust clinical translation. We hope that this review will summarize the current state of emerging applications of machine learning and AI in precision medicine predictive modeling, providing direction for AI-enabled precision cardiovascular oncology—ensuring the effectiveness of cancer treatment while safeguarding long-term cardiovascular health through personalized risk mitigation measures.