Leaf area is an essential indicator of photosynthesis for the study of crop and forest productivity. The Levenberg-Marquardt back-propagation optimization algorithm was coupled with Bayesian regulation to train the artificial neural network (ANN), and the predictive model was developed to determinate rapidly and accurately Moso bamboo leaf area. The results showed that the best input variables were the combination of leaf width and leaf length for ANN model, whereas the leaf shape index did not significantly affect the variability of leaf area. The optimization ANN model possessed with excellent performance and predictable accuracy, with the high determination coefficient of 0.992 and mean relative prediction error of 4.28%. The ANN model would be allowed for estimating accuracy the leaf area of Moso bamboo.