In gas sweetening plants, one of the most sensitive operating parameters is amine circulation rate that must be carefully examined to yield the optimum design for each application. In this study, effort has been made to use computational intelligences for accurate estimating the Monoethanolamine (MEA) circulation rate in amine treating unit. In the first method, optimal topology for feed-forward type neural network, particularly multi-layer perceptron (MLP), has been obtained. In the second method, least square version of support vector machine (LSSVM) algorithm has been employed for the application of interest. Results of this communication demonstrate that the presented models are capable of predicting MEA circulation rate precisely. Since there is no need to define the topology of the LSSVM model in advance, application of this type of modeling is more preferable.