Dairy industry produces enormous amount of cheese whey compromising of major milk nutrients but remains unutilized all over the globe. The present study investigates the production of β-Cryptoxanthin (β-CRX) by Kocuria marina DAGII using cheese whey as substrate. Response surface methodology (RSM) and artificial neural network (ANN) was implemented to obtain the maximum β-CRX yield. Significant factors viz. yeast extract, peptone, cheese whey and initial pH were the input variables in both the optimizing studies and β-CRX yield and biomass were taken as output variables. The ANN topology of 4-9-2 was found to be optimum when trained with feed-forward back propagation algorithm. Experimental values of β-CRX yield (17.14 mg/L) and biomass (5.35 g/L) were compared and ANN predicted (16.99 mg/L and 5.33 g/L respectively) values were found to be more accurate compared to RSM predicted values (16.95 mg/L and 5.23 g/L respectively). Detailed kinetic analysis of cellular growth, substrate consumption and product formation revealed that growth inhibition took place at substrate concentrations higher than 12%(v/v) of cheese whey. Han and Levenspiel model was the best fitted substrate inhibition model that described the cell growth in cheese whey with a R2 and MSE of 0.9982 and 0.00477%, respectively. The potential importance of this study lies in the development, optimization, modelling and characterization of a suitable cheese whey supplemented medium for increased β-CRX production.