Atlantic cod (Gadus morhua L.) is one of the most important fish species in the fisheries industries of many countries; however, these fish are often infected with parasites. The detection of pathogenic larval nematodes is usually performed in fish processing facilities by visual examination using candling or by digesting muscles in artificial digestive juices, but these methods are both time and labor intensive. This article presents an innovative approach to the analysis of cod parasites from both the Atlantic and Baltic Sea areas through the application of rough set theory, one of the methods of artificial intelligence, for the prediction of food safety in a food production chain. The parasitological examinations were performed focusing on nematode larvae pathogenic to humans, e.g., Anisakis simplex, Contracaecum osculatum, and Pseudoterranova decipiens. The analysis allowed identification of protocols with which it is possible to make preliminary estimates of the quantity and quality of parasites found in cod catches before detailed analyses are performed. The results indicate that the method used can be an effective analytical tool for these types of data. To achieve this goal, a database is needed that contains the patterns intensity of parasite infections and the conditions of commercial fish species in different localities in their distributions.