Caligus rogercresseyi is an ectoparasitic copepod that affects salmon farms, generating negative effects on the productivity of the industry. The present study consisted of applying data science and machine learning techniques to data on C. rogercresseyi infection from Salmo salar farming centres in the Aysen region. The multiple regression models show a significant inverse relationship between total C. rogercresseyi infections (females, males, both adults together, and juveniles), with latitude and temperature, and a significant direct relationship with longitude and salinity for the total data. The results agree with observations for infection rates of C. rogercresseyi in farmed salmon in the southern part of Chile.