Childhood cancer survivors face a hidden threat that can emerge years after treatment ends: heart damage from life-saving chemotherapy drugs. With over 400,000 pediatric cancer survivors in the US alone, identifying those at risk before symptoms appear could prevent thousands of cases of heart failure and premature cardiac death. A comprehensive analysis spanning 73 studies reveals that artificial intelligence models achieve approximately 80% accuracy in predicting chemotherapy-induced heart damage in children, outperforming traditional monitoring approaches. Global longitudinal strain measurement through specialized echocardiography showed moderate predictive power at 72% accuracy, while standard blood markers like troponin and NT-proBNP demonstrated inconsistent reliability. Emerging microRNA biomarkers showed preliminary promise but lack standardized protocols for clinical implementation. The analysis confirms what pediatric cardiologists have long suspected: no single test adequately captures the subtle early changes that precede overt heart failure in young cancer survivors. This represents a critical gap in survivorship care, as anthracycline chemotherapy drugs can cause progressive heart muscle damage that may not manifest for decades. The superior performance of AI models likely reflects their ability to integrate multiple data streams and detect complex patterns invisible to conventional analysis. However, the significant variability between AI studies suggests these tools require rigorous validation before clinical deployment. For the growing population of childhood cancer survivors, this research reinforces the need for personalized, technology-enhanced cardiac monitoring strategies that could fundamentally transform long-term survivorship care.