Purpose of Review <p>Cancer therapies significantly increase cardiovascular disease risk, with cancer patients facing up to 42% higher likelihood of developing cardiovascular complications compared to those without cancer. This review examines current applications of artificial intelligence (AI) in cardio-oncology, focusing on pre-treatment risk prediction, detection of cardiovascular dysfunction during and after therapy, and clinical implementation challenges.</p> Recent Findings <p>AI applications have shown promising results across multiple domains. Machine learning models integrating electronic health records, ECG data, echocardiographic findings, and advanced imaging have demonstrated feasibility in identifying high-risk patients before treatment initiation. For detecting cardiac dysfunction, AI-enhanced tools have shown superior performance in identifying subtle cardiotoxic effects, including AI-ECG and- imaging models detecting reduced ejection fraction with high accuracy, automated strain analysis for early dysfunction detection, and AI-guided handheld ultrasound enabling point-of-care monitoring by non-specialists. Despite these advances, significant barriers prevent clinical implementation, including limited high-quality datasets representing diverse oncology populations, lack of standardized outcome definitions, model interpretability challenges with “black box” systems, and insufficient integration with existing clinical workflows.</p> Summary <p>While AI demonstrates substantial potential for improving outcomes through enhanced risk stratification and early detection, current evidence does not yet support widespread implementation. Success will require rigorous&#xa0;prospective validation, improved interpretability, and comprehensive implementation strategies addressing both technical and human factors.</p>