CT-derived fractional flow reserve (FFR-CT) is a noninvasive technique that combines coronary computed tomography angiography (CCTA), with computational modeling to assess the physiological significance of coronary artery stenoses. By integrating anatomical and hemodynamic data, FFR-CT provides lesion-specific pressure estimates comparable to invasive fractional flow reserve, without the need for catheterization or pharmacologic hyperemia. This narrative review summarizes the fundamental principles, validation evidence, clinical applications, and limitations of FFR-CT in contemporary coronary artery disease (CAD) management. FFR-CT is computed using computational fluid dynamics or machine-learning algorithms that simulate hyperemic blood f low within patient-specific coronary geometries reconstructed from standard CCTA images. Major multicenter trials and registries, including DISCOVER FLOW, DeFACTO, NXT, and ADVANCE, have consistently demonstrated high diagnostic accuracy and robust prognostic value, showing strong correlation with invasive FFR and improved specificity compared with CCTA alone. Integration of FFR-CT into diagnostic workflows significantly reduces unnecessary invasive angiography and revascularization procedures while maintaining excellent clinical outcomes. Recent guideline endorsements by the European Society of Cardiology and the American College of Cardiology reflect its growing role as a validated, cost-effective tool in stable CAD evaluation. Despite limitations related to image quality, microvascular assumptions, and restricted utility in acute settings, ongoing advances in artificial intelligence, image reconstruction, and hybrid modeling promise faster, more accurate, and more accessible analysis. FFR-CT represents a paradigm shift in noninvasive coronary physiology assessment, providing a unified anatomic–functional framework that enhances precision in CAD diagnosis and management.