Abstract: Ionic liquids, with their green chemistry attributes and customizable properties, have garnered significant interest as potential additives in sulfuric acid catalyzed C4 alkylation. Given the vast combinatorial possibilities of anion and cation pairs, traditional experimental screening methods fall short due to their inefficiency in exploring extensive chemical spaces. To address this challenge, we have adopted machine learning techniques, notably the random forest algorithm (RF), to establish correlations between the structural attributes of ionic liquids and enhanced acid-hydrocarbon interfacial properties of ionic liquids. This approach has enabled us to develop predictive models based on various descriptors, enhancing our understanding and selection of ionic liquids for specific applications. Moreover, to facilitate the rational design of novel ionic liquid combinations, we have utilized continuous and data-driven molecular descriptors (CDDD) derived from the SMILES codes of the compounds. These descriptors serve as inputs for the Success-History based Adaptive Differential Evolution algorithm (SHADE), which efficiently navigates and decodes the potential space for promising candidates. We have also integrated substructure constraints to ensure the rationality and viability of generated structures. Focusing on key parameters like interfacial thickness (δ_w) and tension (γ) in C4 alkylation, The constructed predictive models achieved a determination coefficient of 0.952 for interfacial thickness and 0.901 for interfacial tension on the test set, demonstrating high prediction accuracy. Moreover, through optimization by the SHADE algorithm, 328 ionic liquid combinations that meet the requirements for interfacial thickness and tension were successfully generated, significantly expanding the feasible domain of ionic liquid combinations that meet the criteria. This not only enhances the capability of predicting acid-hydrocarbon interfacial properties but also provides new methods and ideas for the rational design of ionic liquids.