Infant mortality is characterized by the death of young children under the age of one, and it is an issue affecting millions of children in the world. The objective of this article is to employ concepts of knowledge discovery in databases, specifically of machine learning in the data mining phase, to characterize infant mortality in two states of Brazil: Santa Catarina, with the lowest infant mortality rate of the country’s states, and Amapá, with the highest. The classifiers C4.5, JRip, Random Forest, SVM, and Multilayer Perceptron were used, and a brief comparison of the results obtained by the classifiers in both states is made. In addition, the dataset preprocessing is detailed, which includes attribute selection and class balancing. The results show that the features APGAR5, WEIGHT, and CONGENITAL ANOMALY stood out the most from the rules generated by the tree-based classifiers.