Wastewater treatment plants (WWTP) involve several complex physical, biological and chemical processes. Often these processes exhibit non-linear behavior that is difficult to describe by classical mathematical models. Safer operation and control of a WWTP can be achieved by developing a modeling tool for predicting the plant performance. In the last decade, many studies were realized in wastewater treatment based on intelligent methods which are related to modeling WWTP. These studies are about predictions of WWTP parameters, process control of WWTP, estimating WWTP output parameters characteristics. In many studies, neural network models were used to model chemical and physical attributes in the flow rate. In this Thesis, a data-driven approach for analyzing water quality is introduced. Improvements in the data collection of information system allow collection of large volumes of data. Although improvements in data collection systems have given researchers sufficient information about various systems, they must be used in conjunction with novel data-mining algorithms to build models and recognize patterns in large data sets. Since the mid 1990's, data mining has been successfully used for model extraction and describing various phenomena of interest.