Objective: New tools are urgently needed to help with surgical decision-making in type B aortic dissection (TBAD) that is uncomplicated at the time of initial presentation. This narrative review aims to answer the clinical question, Can computational modeling be used to predict risk in acute and chronic Stanford TBAD? Methods: The review (PROSPERO 2018 CRD42018104472) focused on risk prediction in TBAD. A comprehensive search of the Ovid MEDLINE database, using terms related to computational modeling and aortic dissection, was conducted to find studies of any form published between 1998 and 2018. Cohort studies, case series, and case reports of adults (older than 18 years) with computed tomography or magnetic resonance imaging diagnosis of TBAD were included. Computational modeling was applied in all selected studies. Results: There were 37 studies about computational modeling of TBAD identified from the search, and the findings were synthesized into a narrative review. Computational modeling can produce numerically calculated values of stresses, pressures, and flow velocities that are difficult to measure in vivo. Hemodynamic parameters—high or low wall shear stress, high pressure gradient between lumens during the cardiac cycle, and high false lumen flow rate—have been linked to the pathogenesis of branch malperfusion and aneurysm formation by numerous studies. Considering the major outcomes of end-organ failure, aortic rupture, and stabilization and remodeling, hypotheses have been generated about inter-relationships of measurable parameters in computational models with observable anatomic and pathologic changes, resulting in specific clinical outcomes. Conclusions: There is consistency in study findings about computational modeling in TBAD, although a limited number of patients have been analyzed using various techniques. The mechanistic patterns of association found in this narrative review should be investigated in larger cohort prospective studies to further refine our understanding. It highlights the importance of patient-specific computational hemodynamic parameters in clinical decision-making algorithms. The current challenge is to develop and to test a risk assessment method that can be used by clinicians for TBAD.