We developed an approach that integrates generalized additive model (GAM) and neural network model (NNM)for projecting the distribution of Argentine shortfin squid (Illex argentinus). The data for this paper was based oncommercial fishery data and relevant remote sensing environmental data including sea surface temperature(SST), sea surface height (SSH) and chlorophyll a (Chl a) from January to June during 2003 to 2011. The GAM wasused to identify the significant oceanographic variables and establish their relationships with the fishery catch perunit effort (CPUE). The NNM with the GAM identified significant variables as input vectors was used forpredicting spatial distribution of CPUE. The GAM was found to explain 53.8％ variances for CPUE. The spatialvariables (longitude and latitude) and environmental variables (SST, SSH and Chl a) were significant. The CPUEhad nonlinear relationship with SST and SSH but a linear relationship with Chl a. The NNM was found to beeffective and robust in the projection with low mean square errors (MSE) and average relative variances (ARV).The integrated approach can predict the spatial distribution and explain the migration patt of Illex argentinusin the Southwest Atlantic Ocean.