The overall objective of this study is to design geographic information based decision support system, for sustainable land use assessment in irrigated areas. One of the particular interests in this study is the linkage between geographic information system (GIS), decision support system (088), artificial neural network (ANN) models and knowledge base (KB). The goal of this type of integration is to develop more useful computer tools that can assist in spatial problem solving, not only by conventional computing. But also by some sort of reasoning similar to those of human experts. The developed system is an integrated approach represented by a computer program designed specifically for non GIS users. and could be used by decision-makers, technical advisers, planners, and researchers In a facilitated land use decision making process. The system consists of ten components namely, digital map, mapping object, external database, knowledge base, model base. Internal database. report generator, graph generator. and map generator accessed by a user interface. The results of the KB subsystem validation showed a full agreement with the expert‘s knowledge. The results of the crop water requirement model gave a high correlation (0.998) with those obtained from the FAO*s CrOpWat program for all land uses (crops). The system used two case studies. to test the overall system performance, and generate different kinds of maps, reports and graphs from which in-depth data analysis was possible. One of those cases came from the Sugar Beet area, Egypt, while the other one was obtained from Fuka-Matrouh area, Egypt. The testing results show that the system provides a powerful tool to support the user in mutti—objectives multi-criteria decision-making process for the different alternatives of land use ranking and selection. The developed system was compared with other local and international land evaluation software. It is evident that this system achieved the integration of GIS and intelligent systems. Moreover, the developed software helps in easing and speeding the land evaluation process, as well as mapping the land capability and crop suitability.