For TDS, COD, and TOC, Table 5 shows regression parameters’ ANOVA for the estimated quadratic response surface models, along with other statistical parameters. The total variation in response predicted by this model (TDS = 0.8525, COD = 0.8677, and TOC = 0.8792) is represented by the R² coefficient (TDS = 0.8525, COD = 0.8677, and TOC = 0.8792), which equals the ratio of the sum of squares of regression to their sum total. TDS, COD, and TOC have high R² values (85.25, 86.77, and 87.92%, respectively), implying that the results are consistent. The correlation coefficients should be at least 0.80 for a good model fit. A rational modification of the quadratic model was confirmed by a big R² coefficient to the empirical data. The R² values of 78.88, 85.22, and 82.62% in this study indicate that the regression model was able to shed light on the relationship between the independent variables and the response. The statistical significance of the model was established by determining the model's coefficients. For COD, TDS, and TOC of MLSW, the R² values were 0.8677, 0.8525, and 0.8792, respectively. With ‘Adj R²’ of 0.7888, 0.8922, and 0.8262, the ‘Pred R²’ of 0.6834, 0.8373, and 0.7268 indicated a good arrangement. The signs to noise ratio was calculated using ‘Adeq Precision.’ For this criterion, a ratio of >4 represented favorability. The ratios of 16.007, 19.681, and 17.758 showed a positive sign, indicating that the model can be employed in design space planning. The models were improved by removing the study's irrelevant model terms with limited effect. Thus, the COD, TDS, and TOC response surface models were thought to be reasonable.