Our hypothesis that it is possible to create a ML model to effectively diagnose early feline mammary cancer using blood metabolites was confirmed by our calculations. We developed a Random Forest-based ML model that is able to predict feline mammary carcinoma with 85% accuracy. In addition to the classifier, we studied the metabolic pathways related to feline mammary cancer. We analyzed the relationships between genes that interacted with the blood metabolites. Through the use of bioinformatics tools, we were able to identify that most of the metabolites used to train the ML model corresponded to the pathways that cause feline mammary carcinoma and are interacting with the known FMC-related genes.