<p>Patient-specific computational models exhibit strong concordance with invasively measured fractional flow reserve (FFR)—the clinical gold standard for diagnosing coronary ischemia. However, current modeling techniques frequently rely on computationally intensive assumptions such as pulsatile flow dynamics and often fail to optimally leverage patient-specific clinical data that is routinely available, limiting their practical clinical adoption. In this study, we propose a hybrid coronary angiography-based approach that reduces computational complexity through simplified steady-state flow assumptions, while simultaneously better leveraging available clinical information. Specifically, we integrate physics–based modeling with a machine learning (ML) feedback loop designed to refine and improve FFR predictions. We evaluated this hybrid framework using a retrospective two-center cohort comprising 132 patients with 132 coronary lesions. Our results demonstrate that steady-state models effectively capture essential hemodynamic patterns, closely matching pulsatile model predictions. The ML refinement step enhances diagnostic accuracy, yielding a sensitivity of 83.3%, specificity of 100.0%, positive predictive value of 100.0%, negative predictive value of 88.2%, and overall precision of 92.6%. By effectively combining efficient computational modeling with targeted ML-driven refinements, our approach represents a robust, clinically viable solution for accurate patient-specific FFR estimation.</p>