Hydrogen production is one of main subjects in fuel cells. The traditional method of synthesis gas production is based on fuel reforming using catalysts. The main problem of these methods is sensitivity and fast degradation of catalysts especially when fuels with high sulfur content are used. A new technique for hydrogen production is fuelreforming using non-catalytic filtration combustion in porous media reactors. Various experimental works have been carried out to increase hydrogen production under different operating conditions such as inlet fuel velocity and equivalence ratio. First, we investigated the ability of adaptive neuro fuzzy inference system (ANFIS) for predicting the filtration combustion characteristics. Four distinct ANFIS models were developed for estimating the hydrogen yield and energy conversion efficiency for fuels of jet A and butanol. Eight different membership functions of dsigmf, gauss2mf, gaussmf, gbellmf, pimf, psigmf, trapmf and trimf were tested for training of the ANFIS networks. The results showed that the RMSE of the best developed ANFIS models for estimating of the hydrogen yield of jet fuel, hydrogen yield of butanol, conversion efficiency of jet fuel and conversion efficiency of butanol were 1.399, 1.213, 0.508 and 2.191, respectively. Moreover the R2 values of 0.998, 0.998, 0.999 and 0.999 were obtained for predicting the above mentioned variables, respectively. In the second step, a novel algorithm based on imperialist competitive algorithm (ICA) was used for optimization of hydrogen yield and energy efficiency. The maximum value of hydrogen yield and energy efficiency was 50.46% and 67.88% for jet A and 47.27% and 96.93% for butanol, respectively. The results showed that the imperialist competitive algorithm is an efficient and powerful algorithm to optimize combustion processes.