Sickle cell disease (SCD) is a hereditary hemoglobinopathy defined by the polymerization of hemoglobin S under hypoxic conditions, resulting in the deformation of red blood cells (RBCs) into rigid, sickled shapes. These biomechanical changes profoundly disrupt hemorheological behavior within the microcirculation, driving hallmark complications such as RBC margination, hemolysis, and vaso-occlusion. These events trigger pathological feedback loops that promote endothelial dysfunction and chronic vascular damage. This chapter charts the rise of computational modeling in sickle cell disease, from hemoglobin polymerization to red blood cell biomechanics and microvascular dynamics. It highlights how statistical and machine learning methods extend traditional analyses and how emerging AI-driven approaches may guide new therapies and transfusion strategies. Together, this chapter provides a unified view of how engineering and data-driven approaches complement experimental and clinical research to illuminate the rheological mechanisms of SCD and inform future interventions.