A comprehensive, simple, neural network model was constructed to replace the common semi-empirical mathematical models used for predicting individual O-2 absorption coefficients (K(L)a) within Erlenmeyer and Hinton shake-flasks. Different factors that influence K(L)a within shake-flasks, such as flask dimensions, working volumes, baffle-heights, and shaking speeds, were investigated and the experimental results employed to deduce the mathematical model for each type of shake-flask. Meanwhile, the K(L)a values calculated from the mathematical models were used to derive a non-linear neural network estimator (NNE). The NNE for K(L)a prediction was implemented to evaluate the O-2 absorption effect within the flasks and gave a promising result.