Biohydrogen is a sustainable energy resource due to its potentially higher efficiency of conversion to usable power, non-polluting nature and high energy density. The purpose of modeling and optimization is to improve, analyze and predict biohydrogen production. Biohydrogen production depends on a number of variables, including pH, temperature, substrate concentration and nutrient availability, among others. Mathematical modeling of several distinct processes such as kinetics of microbial growth and products formation, steady state behavior of organic substrate along with its utilization and inhibition have been presented. Present paper summarizes the experimental design methods used to investigate effects of various factors on fermentative hydrogen production, including one-factor-at-a-time design, full factorial and fractional factorial designs. Each design method is briefly outlined, followed by the introduction of its analysis. In addition, the applications of artificial neural network, genetic algorithm, principal component analysis and optimization process using desirability function have also been highlighted. (C) 2011 Elsevier Ltd. All rights reserved.