Nonalcoholic Steatohepatitis (NASH) is a severe form of Nonalcoholic fatty liverdisease, that is caused due to excessive calorie intake, sedentary lifestyle and in the absence of severe alcohol consumption. It is widely prevalent in the United States and in many other developed countries, affecting up to 25 percent of the population. Due to being asymptotic, it usually goes unnoticed and may lead to liver failure if not treated at the right time. Currently, liver biopsy is the gold standard to diagnose NASH, but being an invasive procedure, it comes with it's own complications along with the inconvenience of sampling repeated measurements over a period of time. Hence, noninvasive procedures to assess NASH are urgently required. Magnetic Resonance Elastography (MRE) based Shear Stiffness and Loss Modulus along with Magnetic Resonance Imaging based proton density fat fraction have been successfully combined to predict NASH stages However, their role in the prediction of disease progression still remains to be investigated. This thesis thus looks into combining features from serial MRE observations to develop statistical models to predict NASH progression. It utilizes data from an experiment conducted on male mice to develop progressive and regressive NASH and trains ordinal models, ordered probit regression and ordinal forest on labels generated from a logistic regression model. The models are assessed on histological data collected at the end point of the experiment. The models developed provide a framework to utilize a non-invasive tool to predict NASH disease progression.