The relationship between structures and physical properties for rubber blend was investigated by materials informatics approach. SBR/IR/Silica blend rubber, which is commonly used as tire rubber, was targeted in this paper. Model structures were created on a computer, and physical properties were calculated by finite element method (FEM) simulation. Datasets with structural features as explanatory variables and calculated values as objective variables were obtained by three-dimensional structure analysis for model structures. To clarify the contribution of each structural features to physical properties, machine learning was performed using the dataset with the modulus at 50% elongation (M50) as the objective variable. The prediction accuracy of the model was as high as 0.87 in R² value. Furthermore, SHapley Additive exPlanation (SHAP) analysis revealed that the continuity of SBR phase and the shape of the phase separation interface are particularly important for physical characteristics. Finally, we created a structure-property correlation diagram that allow us to narrow down the compositions and structures that satisfied target physical properties without actual experiments. It follows that material development period will be shortened.