The in sillico method to predict the ecotoxicity of chemical substances for reducing animal testing has become attracted attention. A most common model for predicting ecotoxicity classify chemical substances empirically based on functional groups and then predict ecotoxicity with a linear regression by using a descriptor of a chemical substance such as Log Kow. But the conventional method outputs duplicate result for one chemical substance when it has multiple functional groups. Moreover, this method is not appropriate for predicting the ecotoxicity of metal compounds. To overcome these challenges, this study developed a new fingerprint as a feature set for machine learning, and a new prediction model with supervised machine learning for chronic ecotoxicity on fish. The new fingerprint extracts feature of a chemical substance by judging the existence of the structure contributing to ecotoxicities such as carbamate insecticide, organophosphorus pesticides, organic halogen, various metal elements, and hexavalent chromium. Moreover, we compared the accuracy for predicting chronic ecotoxicity on fish with various machine learning models by 10-fold cross-validation using this new fingerprint, general fingerprints, and descriptors together as a feature set. As a result, our developed method with the stacking ensemble was the most accurate in this study. This method improved accuracy by using the result of multiple machine learning algorithms as a part of a feature set. The result of the benchmark test show that the prediction accuracy of this method was better than conventional methods.