Nanotechnology has led to the development of new materials with unique properties and a wide variety of applications. Meanwhile, it has raised great concerns regarding their properties and potential adverse effects to humans and the environment. In this work, a Quantitative Structure-Activity Relationship (QSAR) modeling study was carried out for predicting the adsorption property of a set of 59 environmental pollutant aromatic compounds into multi-walled carbon nanotubes. We report a systematic evaluation of multiple linear regression (MLR) and artificial neural network (ANN) methods along with a variety of structure representations and feature selection algorithms. Judging from the attained statistical results, our derived QSAR models have an acceptable overall accuracy and robustness, as well as good predictivity on external data. This QSAR study suggested also that the adsorption ability of these compounds is mainly explained by size, charge and hydrophobicity factors. Moreover, it showed to be a simple, precise and credible tool forward-predicting the adsorption of aromatic compounds by multi-walled carbon nanotubes.