Landslides as a morphodynamic processes is one of the major geo-hazard concern in Sweden which is able to harm and affect nearby environments, socio-economy, people and industrial developments. Therefore, landslide susceptibility analysis will be a useful tool for engineers and planners to find safer areas not only for development schemes but also for hazard mitigation. In the current paper, landslide susceptibility map has been assessed for an extended surrounding area around the Göta River in southwest of Sweden by means of integrated an artificial neural networks (ANNs) based model and geographic information system (GIS). A wide range of effective parameters on slope instability were collected and classified into four groups including topographic and geomorphologic features, geological factors, hydrology and hydrogeology parameters as well as land use data. The thematic data were mainly derived from processed satellite images, aerial photographs and digital elevation model (DEM) as well as documentary data to construct the spatial database using GIS. The location of landslides to produce the inventory map of study area also has been identified from documentary, monitored and interpretation of aerial photographs. The weights of involving condition factors in provided susceptibility map was analyzed using the landslides occurrence factors by the ANN model and then validated using by both previous studies and location of experienced landslides in selected area. The high achieved accuracy using ANN model demonstrated a reliable criterion for future studies in landslide susceptibility zonation in this area.