Software engineering principles deal with the quality of software in consideration of various attributes. Software defect prediction is one of the key domains in the field of software engineering. Software defect prediction aims at providing non-defective softwares to the end user. In order to ensure the reliability and robustness of software, defects must be identified at each stage by conducting various review activities. In order to analyze the quality of software various defect prediction models are developed. In this study, a defect prediction model is modeled using Traditional Gray Wolf Optimization (TGWO) and Grid Search GWO (GSGWO). The performance of defect prediction models is analyzed on the basis of Within-Project Defect Prediction (WPDP_GWO), tenfold_GWO, TGWO, and GSGWO. Further, homogeneous cross-project defect prediction model is also developed under the consideration of predictive modeling. The performance of prediction models analyzed using AUC performance metric. Statistical tests are used for the validation of prediction models. Thus, RFGWO performed best for CPDP and GSGWO performed best for WPDP.