Toward green educational building development, windows are important design elements as the source of natural lighting and heating in classrooms. The amount of natural lighting and net heating received by a classroom in a year depends on the school location, weather conditions, as well as the window orientation and size. Schools in Iran consume a considerable amount of energy which is mostly supplied using nonrenewable fossil fuel resources. This energy consumption can be reduced through a well-designed daylighting approach. In this paper, in order to investigate the effects of window characteristics on construction and operational costs of schools, by varying the Window-to-Wall Ratio (WWR) and window orientation, 288 daylighting scenarios are generated for a typical standard classroom in a warm-dry climatic zone in central Iran. The DOE-2 software is utilized to estimate annual gas and electric consumption, for the generated scenarios over a period of 50 years. Considering the operation and construction cost, the best window facing and optimal range of WWR in each orientation is determined for the studied standard classroom. The results of simulated daylighting scenarios are then used to train regression based Support Vector Machines (SVMs) in order to show the feasibility of applying the Support Vector Regression (SVR) as an artificial intelligent system. The obtained results show that SVR as an architectural assistant performs well and the SVR-based predictor can rapidly, easily and accurately predict the operational and construction cost of a classroom just by determining the window size and installation face.