Visual identification of fish eggs is difficult and unreliable due to a lack of information on the morphological egg characteristics of many species. We used random forests machine learning to predict the identity of genetically identified Bighead Carp Hypophthalmichthys nobilis, Grass Carp Ctenopharyngodon idella, and Silver Carp H. molitrix eggs based on egg morphometric and environmental characteristics. Family, genus, and species taxonomic-level random forests models were explored to assess the performance and accuracy of the predictor variables. The egg characteristics of Bighead Carp, Grass Carp, and Silver Carp were similar, and they were difficult to distinguish from one another. When combined into a single invasive carp class, the random forests models were ≥ 97% accurate at identifying invasive carp eggs, with a ≤5% false positive rate. Egg membrane diameter was the most important predictive variable, but the addition of ten other variables resulted in a 98% success rate for identifying invasive carp eggs from 26 other upper Mississippi River basin species. Our results revealed that a combination of morphometric and environmental measurements can be used to identify invasive carp eggs. Similar machine learning approaches could be used to identify the eggs of other fishes. These results will help managers more easily and quickly assess invasive carp reproduction.