Adsorption is recognized as one of the most impactful techniques extensively utilized in worldwide environmental protection activities. Interpreting experimental adsorption isotherm data is essential for predicting the mechanism of adsorption. This study involves the comparison and discussion of linear and non-linear forms of Langmuir, Freundlich, Dubinin-Radushkevich, and Temkin isotherms, which were applied to the experimental data obtained during the adsorption of Rhodamine-B dye by CaO NPs. To determine the best-fitting model in the adsorption isotherms, and to quantitatively illustrate the relevant sorption system, different error functions, and statistical tools including the sum of squares of the errors (ERRSQ/RSS), average relative error (ARE), the sum of absolute errors (EABS), average percentage error (APE/ARED), second-order corrected Akaike information criterion (ALC_c), chi-square test (Chi-Sq/χ²), G², Hybrid fractional error function (HYBRID), a standard procedure called the sum of normalized errors (SNE), and fit tools including the coefficient of determination (R²), student’s T-test, and F-tests were applied. The results indicated that the Langmuir isotherm was identified as more suitable (T-test = 0.999981, F-test = 0.991724664, and R² = 0.98537) than all other linear forms. Likewise, the analysis of non-linear forms provided the Temkin model as more beneficial, with the lowest values of all error functions and the largest values of fit tools, including R₂ (0.94687), T-test (0.969513), and F-test (0.999998). The linear Langmuir model was noted as best fitted with the largest values of fit tools and lowest normalized errors (0.0077098) than the non-linear Langmuir isotherm (0.306951).  It was concluded that the linear Langmuir adsorption isotherm has the lowest values of all error functions, including SNE, and the highest values of fit tools among linear forms of each adsorption model. Moreover, the non-linear Temkin isotherm was determined as the best-fitting model among all non-linear forms, with the minimum values of all error functions, and maximum values of good fit tools.