The present study focuses on the development and validation of three chemometric models that are simple, accurate, and precise for the determination of levamisole and triclabendazole. These models overcome the need for prior separation techniques. The approaches under consideration are classical least squares (CLS), partial least squares (PLS-1), and principal component regression (PCR). The second derivative of the absorbance values of the calibration set were computed by MATLAB to construct the models. A 2-factor 5-level experimental design was employed to construct a set of 25 mixtures, each consisting of varying proportions of levamisole and triclabendazole. Thirteen mixtures were utilized as the training set, while the remaining twelve combinations were designated as the validation set. The utilization of multi-wavelengths in place of single wavelength spectrophotometry has significantly enhanced the precision and prediction capabilities of these multivariate methods. The proposed approaches have been shown to be reliable and exact, and they may be applied to the determination of the drugs in both their pure form and pharmaceutical formulations.