Management of water quality is critical issue in modern times. Water quality determination is highly sensitive. The term quality ascribed to water is fuzzy as it is only a relative term. To evaluate water quality, the goal is to establish a link between the metrics. It is vital to have a reliable model to forecast the state of water quality. A broader description would be that rising cations and anions are the source of rising Total Dissolved Solids (TDS). To precisely forecast numerical values of TDS that match the values of cations and anions, a mathematical model cannot be created. The inorganic components of TDS include dissolved gases, a small amount of organic stuff, and carbonates, bicarbonates, sulphates, chlorides, and phosphates of calcium, magnesium, potassium, and iron, among other elements. Hence, it can act as the best consequent to assess water quality since there is positive correlation between the ions present and TDS. Determining of the water quality due to contamination is highly unpredictable in exact numerical terms. With the aid of previous experience, it may be necessary to generate additional linguistic expressions to define related water quality parameters. The capability of fuzzy logic to develop qualitative model to characterise by appropriate reasoning dwells here. It facilitates influential tool to model uncertainty analogous with imprecision and vagueness. Fifteen parametric variables that include Total Dissolved Solids, Chloride, Conductivity, Fluoride, Hardness, pH, Sodium, Potassium, Iron, Calcium, Magnesium, Bicarbonate, Carbonate, Nitrates and Sulphates are dealt in the present study. Experimental output is modelled in seven sharp stages. One antecedent and one consequent model is developed to bring about a relationship in the first five steps. Two antecedents and one consequent model is developed in the final two steps. In total, seven Fuzzy Inference Systems are built. An attempt is also made to establish sensitivity of parameters. Cause-effect relationship is also dealt in the current work. Comparing the results, TDS and Total Hardness turn to be very good parameters to determine water quality. It is concluded that, fuzzy inference system furnishes a sagacious way to apprehend uncertainty in relationships amid parameters that assess water Quality giving lesser error. Based on the water quality values, the authorities/ policy makers should frame the regulations in accord so as to monitor the health and wealth of the community in an economic and efficient way.