Abstract: Myocardial ischemia represents the central pathological aspect in the development and adverse outcomes of coronary atherosclerotic heart disease. Early and accurate identification, along with individualized interventions, play a decisive role in determining reperfusion time, myocardial preservation, and long-term cardiovascular events. Traditional diagnostic methods rely on electrocardiograms (ECGs), imaging techniques, and serological markers. Nevertheless, they are constrained by issues related to sensitivity and specificity, often resulting in missed diagnoses or misinterpretations. In recent years, artificial intelligence (AI), particularly deep learning and modern machine learning algorithms, have offered novel approaches for the diagnosis, risk prediction, and treatment optimization of myocardial ischemia. By extracting high-dimensional features from large-scale, multi-modal medical data, AI has shown great potential. In the analysis of electrocardiograms, AI can automatically detect minute time-frequency characteristics, enabling the identification of occult ischemia, continuous monitoring, and multi-task risk prediction. In the realm of imaging, significant breakthroughs have been achieved in the automatic segmentation, quantification, and functional evaluation of coronary computed tomography (CT), cardiac magnetic resonance imaging (MRI), and nuclear medicine perfusion imaging. Additionally, by integrating radiomics and multi-modal features, the predictive ability for cardiovascular events has been enhanced. The integration of multi-modal data further strengthens the discrimination of short-term events and long-term cardiovascular risks, providing detailed support for individualized decision-making. In the context of treatment and prognosis management, AI can assist in intraoperative stent implantation planning, postoperative surveillance, and the optimization of pharmaceutical regimens, thus establishing a closed-loop system for diagnosis, treatment, and follow-up. However, several challenges remain to be addressed for widespread clinical implementation, including issues related to model interpretability, cross-center generalization, and data integration. Through a comprehensive review of relevant domestic and international literature, this paper demonstrates that AI holds great promise as a key instrument in the prevention and treatment of myocardial ischemia. It can provide robust support for precision medicine and evidence-based decision-making, while also opening up new avenues for precision medical research.