A fuzzy prediction model has been developed considering the concentration of acetic acid (pH), temperature, and biopolishing time as input variables, while the color change, measured with DEcmc, between samples before and after biopolishing, was used as the output variable. The parameters influencing the color change in knitted cotton fabrics exhibit significant non-linearity. The fuzzy inference system proves to be an effective modeling tool, capable of representing non-linear relationships with a limited amount of experimental data. For the variables, triangular and trapezoidal membership functions were adopted, and a total of 27 rules were established in this research. It was observed that the impact of cellulase concentration on color change is relatively low, but it is strongly influenced by temperature, even at a constant concentration of cellulase. The model developed in this study was validated with an additional experimental data set. The developed system is capable of predicting shade changes with an accuracy of over 90%, which helps to reduce rework and reprocessing in the wet processing sectors.