Expectation to materials informatics increases toward innovative materials development. Materials informatics is the methodology based on numerical simulation and data mining using machine learning. The objective of this report is to mention an introductive effort for the materials informatics of filled rubber. At first, two data mining techniques i.e. self-organizing map and decision tree which is a kind of machine learning are presented to show their effectiveness with the example of the multi-objective design exploration for the tire shape design. The case for microstructure designs of rubber material becomes the same procedure, too. Next, we mention the other important issues for the materials informatics of materials having microstructure i.e. characterization of morphology and construction of simulation model using the morphological parameters. Morphological measures and random morphological modeling are introduced for their purpose.