Malaria was a major health problem in the first half of the 20th Century in mainland Portugal. Nowadays, although the disease is no longer endemic, there is still the risk of future endemic infections due to the continuous occurrence of imported cases and the possibility of transmission in the country by Anopheles atroparvus Van Thiel, 1927. Since vector abundance constitute one of the foremost factors in malaria transmission, we have created several habitat suitability models to describe this vector species' current distribution. Three different correlative models; namely (i) a multilayer perceptron artificial neural network (MLP-ANN); (ii) binary logistic regression (BLR); and (iii) Mahalanobis distance were used to combine the species records with a set of five environmental predictors. Kappa coefficient values from k-fold cross-validation records showed that binary logistic regression produced the best predictions, while the other two models also produced acceptable results. Therefore, in order to reduce uncertainty, the three suitability models were combined. The resulting model identified high suitability for An. atroparvus in the majority of the country with exception of the northern and central coastal areas. Malaria distribution during the last endemic period in the country was also compared with the combined suitability model, and a high degree of spatial agreement was obtained (kappa = 0.62). It was concluded that habitat suitability for malaria vectors can constitute valuable information on the assessment of several spatial attributes of the disease. In addition, the results suggest that the spatial distribution of An. atroparvus in the country remains very similar to the one known about seven decades ago.