We demonstrate that by combining miniaturized high-throughput screening methods with machine learning based decision making algorithms, we achieve an optimization of materials properties in a high-dimensional materials space of approximately 13440 possible hydrogels within 13 experiments, consuming in total only 0.65 mL of stock solution and less than 170 mg (836 µMol) of monomer and crosslinker materials. After the initial screening of 918 materials, the ML-guided approach allows us to expand the space of achievable combinations of pad size, intensity and degradation speed significantly with just additional 79 new material compositions.