Groundwater contamination is one of the major environmental risks related to landfills. Basic information about the behavior of pollutants in soil-groundwater is needed in order to evaluate the migration of leachate from landfills and to establish efficient groundwater monitoring systems. However, in practice, it is very difficult to get exact subsurface data. Thus, modeling the behavior of pollutants during the flow of leachate through soil is important in predicting the fate of the pollutants. In this study, a one-dimensional transport model with advection and dispersion was used as the deterministic model of benzene leachate transport from an industrial landfill. A particle filtering (PF) with sequential importance resampling (SIR) filter and discrete Kalman filtering (KF) were proposed to improve the prediction of the benzene plume transport. A traditional root mean square error (RMSE) of benzene concentration is used to compare the effectiveness of the KF, PF, and a conventional numerical model. The results showed that Kalman filtering outperformed Particle filtering in the initial time steps. Both KF and PF can reduce the error up to 80% in comparison to a conventional numerical approach.