The current review purpose is to present a general overview of different experimental design methods that are applied to investigate the effect of key factors on dark fermentation and are efficient in predicting the experimental data for biological hydrogen production. The methods of two levels full and fractional factorials, Plackett–Burman, and Taguchi were employed for screening the most important factors in dark fermentation. The techniques of central composite, Box–Behnken, Taguchi, and one factor at a time for optimization of the dark fermentation were extensively used. Papers on the three levels full and fractional factorials, artificial neural network coupled with genetic algorithm, simplex, and D-optimal for the optimization of the dark fermentation are limited, and no paper on the Dohlert design has been reported to date. The artificial neural network coupled with genetic algorithm is a more suitable method than the RSM technique for the optimization of dark fermentation. Literature shows that the optimization of critical factors plays a significant role in dark fermentation and is useful to improve the hydrogen production rate and hydrogen yield.