Uptake of contaminants from the groundwater is one pathway of interest, and efforts have been made to relate root exposure to transloation throughout the plant, termed the transpiration stream concentration factor (TSCF). This work utilized machine learning techniques and statistcal analysis to improve the understanding of plant uptake and translocation of emerging contaminants. Neural network (NN) was used to develop a reliable model for predicting TSCF using physicochemical properties of compounds. Fuzzy logic was as a technique to examine the simultaneous impact of properties on TSCF, and interactions between compound properties. The significant and effective compound properties were determined using stepwise and forward regression as two widely used statiscal techniques. Clustering was used for detecting the hidden structures in the plant uptake data set. The NN predicted the TSCF with improved accuracy compared to mechanistic models. We also delivered new insight to compound properteis and their importance in transmembrane migration. The sensitivity analysis indicated that log K_ow, molecular weight, hydrogen bond donor, and rotatable bonds are the most important properties. The results of fuzzy logic demonstrated that the relationship between molecular weight and log K_ow with TSCF are both bell-shape and sigmoidal. The employed clustering algorithms all discovered two major distinct clusters in the data set.