Research on traumatic brain injury (TBI) has received a great deal of attention owing to the high rate of incidence observed in recent conflicts. Experimental studies of blast and impact effects on the brain often use soft gel materials as surrogates to mimic the mechanical responses of brain tissue. These gels are viscoelastic; exhibiting both viscous and elastic characteristics when undergoing deformation. The viscosity gives the gel a strain rate dependence, as well as, the ability to dissipate energy. To properly model a gel brain’s behavior using a finite element (FE) model, material parameters such as long-term modulus and relaxation time are required for the viscoelastic material model. Traditionally these material parameters were obtained by fitting material characterization data from stress relaxation or oscillatory shear experiments at high frequencies. These experiments were found to be costly and error-prone since gel behavior is nonlinear and depends strongly on factors such as sample size, temperature and curing process. The goal of this work is to use an integrated approach combining experiment and simulation to efficiently determine the mechanical properties of a gel at finite strains, over a wide range of strain rates. The experimental side of this technique subjects a constrained block of gel to high strain rate loading while the response is captured via high-speed video. The corresponding computational models with variable viscoelastic gel properties were subject to the same experimental loading conditions. Using optimization analysis to compare dynamic deformations between the simulations and the experimental data, we can calibrate for the viscoelastic parameters of the gel. We have validated such an integrated approach on a Sylgard gel with a known set of material parameters. We also compared the response of the same gel exposed to shock wave loading via a shock tube and used simulation to confirm its applicability for the blast condition.