Coronary computed tomography angiography (CTA) provides high-resolution imaging of coronary artery disease (CAD), revealing stenosis, plaque characteristics, and hemodynamic markers like CT-derived fractional flow reserve (CT-FFR), axial plaque stress (APS), and wall shear stress (WSS). However, CTA alone has limitations in evaluating the functional significance of lesions, especially in cases with borderline stenosis, severe calcification, or coronary microvascular dysfunction (CMD). CT myocardial perfusion (CTP) complements CTA by directly assessing myocardial blood flow (MBF) and perfusion reserve (MPR), enhancing diagnostic accuracy. Despite its promise, integrating CTA and CTP for comprehensive CAD assessment remains a challenge. Key gaps include the lack of long-term evidence on combined CTA/CTP findings, particularly in incorporating plaque markers with perfusion deficits. Standardizing ischemic thresholds and validating automated CTP analysis tools remain ongoing challenges. CTP also improves specificity in cases where CTA may overestimate ischemic burden and detects microvascular dysfunction, especially in patients with ischemia and no obstructive CAD (INOCA). Quantitative parameters like MBF have been linked to major adverse cardiovascular events (MACE), but issues with protocol variability and cost-effectiveness persist. This study, a real-world, single-center observational cohort, evaluates the clinical utility of integrated CTA/CTP imaging in CAD management. It will assess diagnostic synergy, impact on clinical decisions, and prognostic value in predicting 5-year MACE. AI-driven imaging analysis will quantify plaque features and myocardial perfusion defects, integrating multimodal parameters to generate individualized ischemia risk scores. The goal is to refine non-invasive diagnostic pathways and improve CAD management strategies.