Mineral flotation using microorganisms and/or their derived products is called "bioflotation." This is a promising process due to its low environmental impact; however, it is also a very complicated process, due to its multidisciplinary character, involving mineralogy, chemistry, and biology. So, the optimization of this process is an important challenge. This study assessed the implementation of a quadratic model and an artificial neural network (ANN) for the optimization of hematite and quartz floatability and recovery. The flotation process was carried out using a biosurfactant extracted from the Rhodococcus erythropolis bacteria. Quadratic model was adjusted by genetic algorithms techniques and validated using analysis of variance (ANOVA). Multilayered feed-forward networks were trained using a backpropagation algorithm, implemented using MATLAB R2017a. The topologies of the neural networks included 2 neurons in the input layer and 1 neuron in the output layer in both models, while the hidden layer varied according to the performance of the model. The results showed that the ANN model can predict the experimental results with good accuracy, when compared to quadratic model. Sensitivity analysis showed that the studied variables (pH and biosurfactant concentration) have an effect on the mineral recovery.