Stroke remains a leading cause of global mortality and disability, affecting millions of individuals annually. Computational modeling quantifies the complex pathophysiology of an ischemic stroke, paving the way for personalized therapeutic strategies. This review explores the transformative role of computational modeling and simulation in advancing ischemic stroke research and clinical translation. We systematically examine multi-scale mathematical models, including low-dimensional (0D/1D) hemodynamic representations, high-fidelity 3D computational fluid dynamics (CFD), and fluid-structure interaction (FSI) simulations, to elucidate key hemodynamic parameters such as wall shear stress (WSS), the oscillatory shear index (OSI), and the endothelial cell activation potential (ECAP), which are critical in thrombosis, plaque stability, and stroke progression. Furthermore, we highlight the application of these models in optimizing acute ischemic stroke treatments, including intravenous thrombolysis and mechanical thrombectomy, and in pioneering emerging strategies such as in silico trials and milli-spinner thrombectomy. Despite significant progress, challenges remain in standardization, real-time clinical integration, and model validation. Looking forward, we discuss the integration of multi-scale modeling, artificial intelligence, and patient-specific data toward the development of predictive digital twins and personalized therapeutic frameworks. By bridging mechanistic insights with clinical innovations, computational approaches are poised to redefine stroke care, thus enabling more precise, timely, and effective interventions.