SPSS statistical analysis tools can reduce the complexity of a large data set and simplify them into certain correlation factors for the ease of recognition. Besides, temporal data can be analyzed for prediction based on its temporal trend. In this study, principal component analysis and cluster analysis were employed to anatomize the influence of landfill leachate on groundwater quality in Tainan area. The obtained results are expected to apply to the improvement of landfill management and the pre-warning of groundwater monitoring. The monitoring data of groundwater quality originated from regular monitoring data of landfill monitoring wells by Tainan EPB and groundwater quality monitoring data of "National Water Quality Monitoring Network" by Taiwan EPA. Monitored groundwater quality including total organic carbon, ammonia, nitrate, total hardness, sulfate and chloride were subjected to statistical analysis. The results of principal component analysis (PCA) showed that salinization and organic pollution are two major factors affecting groundwater quality in Tainan area. Based on PCA results of monitoring wells at landfill sites, landfills were classified into four pollution types as sulfate, chloride, nitrate and total organic carbon. Time series analysis tool was utilized to anatomize temporal groundwater data for forecasting. The Sin-Ying fourth landfill was selected as an example. The best models for forecasting groundwater quality are ARMA(1,1) for total organic carbon and nitrate, ARMA(2,1) for ammonia and chloride, and ARIMA(1,1,1) for sulfate and electric conductivity. Based on the temporal trend of ammonia, the influence of landfill leachate on groundwater quality will become much less after 11years.