The search for new larvicides suited for vector control of mosquitoes requires considerable time, an enormous budget, and several analytical setups. Fortunately, the use of quantitative structure–activity relationship (QSAR) modeling allows the prediction of the larvicidal activity of structurally diverse chemicals against mosquitoes in a way quick and costless. This approach can be helpful to study for making biolarvicide with highest ability to destroy mosquito larvae. We propose a quantitative structure-activity relationship model using two different statistical methods, multiple linear regression (MLR) and Support vector machine (SVM) for predicting the larvicidal activity of 30 compounds of essential oils (EOs) isolated from the root of Asarum heterotropoides against Culex pipiens pallens. A model with four theoretical descriptors derived from Dragon software was developed applying the genetic algorithm (GA)-variable subset selection (VSS) procedure. The statistical parameters, R2 = 0.9716, Q2LOO = 0.9595, s = 0.1690 of the model developed by MLR showed a good predictive capability for log LC50 values. The comparison between the results of MLR and SVM models showed that the SVM model present a good alternative to construct a QSAR model for the prediction of the larvicidal activity.