Adsorption parameters used in the modeling of reactive solute transport are usually inferred from laboratory tests such as batch adsorption tests. Experimental data of adsorbed solute, S, and equilibrium concentration, Ce, are measured and then used to infer coefficients that control the adsorption through adsorption isotherm models using fitting techniques. However, as a result of the high costs of chemical analysis and the complexity of the experimental setup, the number of batch tests is usually small and also the observed data is subject to measurement error. Because of that, the fitting of the experimental data to the adsorption isotherm models is never exact, and it can be even worse when the number of samples is small. In this research, we use the normal-score ensemble Kalman filter method to estimate the adsorption parameters, instead of using the standard fitting techniques. Our objective is to quantify the uncertainty in estimation of adsorption isotherms parameters assessing how prior uncertainty is reduced as new samples are collected and also how the final uncertainty is affected by the magnitude of the measurement errors. The method is applied to synthetic examples of non-linear isotherm parameter inference. Langmuir sorption coefficient, k1, and Freundlich sorption coefficient, b1, will be derived from S and Ce, assuming non-linear adsorption models. At the end we compare our method for parameter estimates with the current practice in which the uncertainty is not accounted for.