This article explored the feasibility of Fuzzy Cognitive Maps (FCM) as a tool in the diagnosis and prevention of canine dirofilariasis, a parasitic disease transmitted by mosquitoes that affects dogs in tropical and subtropical regions. Through an exhaustive review of the literature and analysis of previous applications, a methodological proposal was developed for the implementation of FCM in modeling the factors influencing the spread of the disease. Various hypothetical scenarios were designed based on different geographical and epidemiological conditions in Ecuador, aiming to validate the applicability of FCM in predicting outbreaks and optimizing prevention and treatment strategies. The results showed that FCM were effective in identifying high-risk areas, allowing the prioritization of interventions such as vector control, periodic deworming, and community education. Validation with historical prevalence data from regions such as Quito, Loja, Manabí, and Guayaquil confirmed that the model predictions aligned with trends observed in real outbreaks, highlighting the maps\u27 ability to guide public veterinary health policies. In conclusion, FCM proved to be a useful and adaptable tool for managing canine dirofilariasis, providing a comprehensive approach that optimizes resource use and improves intervention strategies
