In this study, the Aspen Plus simulator was used to develop a circulating fluidized bed (CFB) gasifier/steam turbine/proton-exchange membrane (PEM) fuel cell integrated system. Since integrated systems comprise many thermochemical, biochemical, and physical processes, equipment, chemicals, etc., determining output parame-ters is challenging and important. In this context, twenty torrefied biomass samples were parametrically analyzed for syngas properties and H2 production rates. So, using solid fuel characteristics and gasifier operating parameters, a data set including PEM fuel cell module outputs was created. Thereafter, the created data set was utilized to train the artificial neural network (ANN) model. This paper, as far as we know, examines the impacts of different torrefied biomass samples on PEM fuel cell outputs for a sophisticated integrated system dependent on gasification conditions, and provides a more generalized and rapid prediction model for the integrated system with complicated equations. Additionally, parametric studies assist in determining the proposed new integrated system's minimal operating condition, which is highly dependent on the fuel characteristic. High steam/fuel ratio, high carbonization degree, and low pressure lowered PEM efficiency while increasing power and voltage outputs. The ANN model also accurately forecasts PEM fuel cell output parameters (R-2 greater than 0.99 and MAPE less than 1%) based on torrefied biomass proximate analysis data and gasification process operating parameters. As a consequence, a CFB gasifier/steam turbine/PEM fuel cell system, which contains diverse modules and thermochemical processes, can be examined using ANN models trained on a large and high-quality dataset.