Nowadays, modeling is used for evaluating and controlling the weft crimp percentage before and after manufacturing plain woven fabrics. Also, modeling assists in estimating and evaluating crimp percentage without complex and time-consuming experimental procedures. The purpose of this study is to develop a linear regression model that can be employed for the prediction and evaluation of the weft crimp percentage of plain woven fabric. For this study, nine plain woven fabrics of 100% cotton were produced with three different wefts thread densities and weft yarn linear densities. From the findings, the effects of weft count and weft density on the weft crimp percentage of the fabrics were found to be statistically significant with a confidence interval of 95%. The weft crimp percentage showed a positive correlation with weft count and weft density. The weft count and weft density have multicollinearity in the model because the variance inflation factors (VIFs) values are greater than one, which are 1.70 & 1.20, respectively. The model was tested by correlating measured crimp percentage values obtained with a crimp tester instrument to the crimp percentage values calculated by a developed linear model equation. The result disclosed that the model was strongly correlated, with a confidence interval of 95% (R² of 0.9518). Furthermore, the significance value of the t-test is not significant for both the measured weft crimp percentage values and the calculated weft crimp percentage values, which means that they do not differ significantly. Crimp percentage is impacted by fiber, yarn, fabric structural parameters and machine setting parameters. This makes the crimp percentage difficult to control and study, but this developed model can be easily used by manufacturers or researchers for controlling and studying purposes. Thus, the model can be used to produce a fabric with a pre-controlled weft crimp percentage. It can also be used to evaluate and predict the weft crimp percentage before and after fabric production.