AbstractAs property and process models with many variables need to be considered, integrated computer‐aided molecular and process design (CAMPD) problems are computationally expensive. An efficient CAMPD approach is proposed for the simultaneous design of solvents and extractive distillation (ED) processes based on a data‐driven modeling strategy. First, artificial neural network (ANN)‐based process models are trained to replace the physical models conventionally used in CAMPD. Subsequently, optimization is performed to maximize process performance, through which optimal solvent properties and corresponding optimal process parameters are obtained. Then, real solvents approximating the optimal property values are identified from a large solvent database. Rigorous simulations of the ED process are performed to evaluate the performance of the optimal solvents and corresponding process parameters. Further economic evaluation (6.11% lower annual cost compared to the benchmark process) and chemical hazard assessment confirm that acetylacetone is a promising solvent for the ED separation of 1‐butene from 1,3‐butadiene.