The main objective in this paper is to develop a mathematical model of the glucose to gluconic acid batch fermentation process utilizing novel techniques such as Genetic Programming and Artificial Neural Networks. Downloaded experimental data incorporating the effects of the substrate (glucose) and biomass concentrations, and the dissolved oxygen content have been used to model the fermenter. Three Genetic Programming variants: the GPLAB (a matlab toolbox), GP-OLS (a hybrid GP and Orthogonal Least Square method) and GP-Eureqa, as well as Multi-Layer Perceptron Neural Network have been used and compared.