Simulation models have been used successfully to forecast productivity of cropping systems under various weather, management and policy scenarios. These models have helped farmers make efficient resource allocation decisions. However, in Kenya simulation models have not been used extensively and more specifically in modeling maize cropping system. The study aimed at forecasting productivity and profitability of maize cropping system in Uasin Gishu district, Kenya. Both primary and secondary data were used. Both time series and cross-sectional data for variables of interest were collected and complemented by a survey of 20 maize farmers who were systematically selected to verify information obtained from secondary sources. Cropping Systems simulation model and Monte Carlo simulation were used to determine maize output and profits under alternative price scenarios. Even though, simulated yields underestimated actual maize yield both at the district and across the four agro-ecological zones, the deviation from the actual yield was marginal. It is recommended that Cropsyst and Monte Carlo models be included among a bundle of tools for decision making. Further research is also required to test the two models under different locations, soil types, management styles and scales of production.