Sorption is a key process to understand the environmental fate of pollutants on soils, conduct preliminary risk assessments and fill information gaps. Quantitative Structure-Activity Relationships (QSAR) and Pedotransfer Functions (PTF) are the most common approaches used in the literature to predict sorption. Both models use different outcomes and follow different simplification strategies to represent data. However, the impact of those differences on the interpretation of sorption trends and application of models for regulatory purposes is not well understood. We conducted a systematic review to contextualize the requirements for developing, interpreting, and applying predictive models in different scenarios of environmental concern by using pesticides as a globally relevant organic pollutant model. We found disagreements between predictive model assumptions and empirical information from the literature that affect their reliability and suitability. Additionally, we found that both model procedures are complementary and can improve each other by combining the data treatment and statistical validation applied in PTF and QSAR models, respectively. Our results expose how relevant the methodological and environmental conditions and the sources of variability studied experimentally are to connect the representational value of data with the applicability domain of predictive models for scientific and regulatory decisions. We propose a set of empirical correlations to unify the sorption mechanisms within the dataset with the selection of a proper kind of model, solving apparent incompatibilities between both models, and between model assumptions and empirical knowledge. The application of our proposal should improve the representativity and quality of predictive models by adding explicit conditions and requirements for data treatment, selection of outcomes and predictor variables (molecular descriptors versus soil properties, or both), and an expanded applicability domain for pollutant-soil interactions in specific environmental conditions, helping the decision-making process in regard to both scientific and regulatory concerns (in the following, the scientific and regulatory dimensions).