Multiple imputation (MI) approaches have been increasingly popular for providing sound statistical methods to account for missing data. When conducting MI, it is suggested that imputation models be as general as data allow them to be, in order to accommodate a wide range of subsequent analyses of imputed data sets. This requires all relationships that are going to be investigated in any subsequent analysis, such as nonlinearities and interactions, to be included in the imputation model. Unfortunately, traditional MI methods, such as the multivariate imputation by chained equations (MICE), are built on parametric imputation models. These models are often not flexible enough to capture interactions and nonlinearities in high dimensional and large scale data settings. Unlike parametric models, machine learning techniques (MLTs) are model-free methods, and thus provide flexibility for missing data imputation. We propose novel MI methods based on MLTs and compare these methods with MICE through a simulation study.