Multiple sclerosis (MS) susceptibility is multi-factorial with prominent genetic and non-genetic risk components, and there are complex interactions within and amongst these components that additively and synergistically contribute to MS risk. Efforts to characterize these risk components, and identify specific relationships underlying MS risk has significantly accelerated in the era of big-data. The challenge has been how best to analyze these rich and often-times unwieldy data, particularly when the number of predictors likely out-number the number of observations or where there are complex correlation patterns amongst predictors. Machine learning algorithms are well-suited for interrogating these complex big-data, as they rely on minimal assumptions. In general, a machine learning algorithm first parses the data, learns from it, and then assesses the prediction of what was learnt. We have successfully used machine learning to identify promising metabolomic candidates and complex genetic patterns contributing to MS risk. In both studies, Random forests (a supervised machine learning algorithm) was used to identify highly informative predictors for MS, and the relationships between these predictors and MS risk were formally tested using standard statistical models. Thus, we will present findings from two studies where machine learning was used as a means of data reduction which allowed for conservation of statistical power for association testing.