Over the past decade, exposure-response analysis has become an integral part of clinical drug development and regulatory decision-making. The current practice of exposure-response analysis typically relies on parametric modeling and involves step-wise procedures consisting of structural model selection, covariate selection, model fitting and model prediction. However, this current practice is subject to multiple issues such as model mis-specification and error propagation. In this presentation, we will discuss the application of random forest in exposure-response analysis along with its challenges and solutions. A new method utilizing both random forest and parametric modeling is proposed. Simulation results comparing the performance of the proposed method with existing approach will be presented.