For polymers and their composites, processing conditions and the resultant microstructures are crucial in determining their properties. Traditional machine learning (ML) methods typically focus on establishing direct relationships between processing parameters and material properties, often overlooking the critical intermediate step of how processing influences microstructure, limiting the predictive accuracy. In this study, we introduce an approach that first establishes a detailed relationship between processing parameters and the resultant microstructure, and then uses transfer learning and feature fusion to integrate this relationship into the prediction of material properties. Using carbon black-reinforced rubber composites (CRC) as an example, we compared ML models in predicting mechanical properties from processing data. A multi-task deep neural network performed best achieving an R2 of 0.763 with only processing data as input. When incorporating transfer learning and feature fusion, the R2 improved to 0.852 and 0.878, respectively. Shapley explanation analysis validated our approach, highlighting the importance of integrating processing, microstructure, and properties in ML models. This method emphasizes the critical effect of comprehensively considering processing–(micro)structure–property (P–S–P) relationships, leading to more accurate predictions in polymer composite research.