A new dimension of learning lessons from the occurrence of hazardous events involving dangerous substances is considered relying on the availability of representative data and the significant evolution of a wide range of machine learning tools. The importance of such a dimension lies in the possibility of predicting the associated nature of damages without imposing any unrealistic simplifications or restrictions. To provide the best possible modeling framework, several implementations are tested using logistic regression, decision trees, neural networks, support vector machine, naive Bayes classifier and random forests to forecast the occurrence of the human, environmental and material consequences of industrial accidents based on the EU Major Accident Reporting System's records. Many performance metrics are estimated to select the most suitable model in each treated case. The obtained results show the distinctive ability of random forests and neural networks to predict the occurrence of specific consequences of accidents in the industrial installations, with an obvious exception concerning the performance of this latter algorithm when the involved datasets are highly unbalanced.