Abstarct: The c-Met receptor tyrosine kinase is one of the key targets involved in signaling pathways associated with tumor growth and progression. Although crizotinib is the first FDA-approved inhibitor targeting this receptor, the emergence of drug resistance and its pharmacokinetic limitations have encouraged researchers to develop novel compounds. In this context, tetrahydrobenzo[d]thiazol-2-yl derivatives have attracted considerable attention as promising c-Met inhibitors due to their structural flexibility and ability to establish multiple molecular interactions. However, experimental evaluation of the biological activity of these compounds is both time-consuming and costly.In the present study, the Smoothly Clipped Absolute Deviation (SCAD) penalized regression method was employed to develop a Quantitative Structure–Activity Relationship (QSAR) model for newly designed tetrahydrobenzothiazole derivatives. The SCAD approach was simultaneously used for molecular descxriptor selection and construction of a linear predictive model. After model development, its performance was evaluated using statistical indices including the coefficient of determination (R²), cross-validated coefficient (Q²), and mean squared error (MSE). The developed model achieved an R² value of 0.92 for the external test set, a leave-one-out cross-validation coefficient (Q²) of 0.67, and an MSE value of 0.11, indicating good model fit, high predictive ability, and satisfactory robustness. Subsequently, baxsed on the SCAD-QSAR model and the selected key molecular descxriptors, several novel tetrahydrobenzothiazole derivatives were designed with the aim of improving anticancer activity. To further evaluate the potential of the proposed compounds and investigate their binding modes within the active site of the c-Met receptor, molecular docking simulations were performed. The docking results demonstrated that the designed compounds were capable of forming favorable non-covalent interactions, including hydrogen bonds, van der Waals interactions, and other stabilizing interactions with amino acid residues located in the receptor binding pocket. These interactions are expected to enhance both binding affinity and biological activity. Overall, the strong agreement between the predictions of the SCAD-QSAR model and the molecular docking results demonstrates that the proposed computational strategy possesses high predictive accuracy and reliability and can serve as an effective and rational approach for the design and identification of novel c-Met inhibitors baxsed on tetrahydrobenzothiazole derivatives.