This study tested the use of machine learning techniques for the estimation of above-ground biomass (AGB) of Sonneratia caseolaris in a coastal area of Hai Phong city, Vietnam. We employed a GIS database and multi-layer perceptron neural networks (MLPNN) to build and verify an AGB model, drawing upon data from a survey of 1508 mangrove trees in 18 sampling plots and ALOS-2 PALSAR imagery. We assessed the model's performance using root-mean-square error, mean absolute error, coefficient of determination (R-2), and leave-one-out cross-validation. We also compared the model's usability with four machine learning techniques: support vector regression, radial basis function neural networks, Gaussian process, and random forest. The MLPNN model performed well and outperformed the machine learning techniques. The MLPNN model-estimated AGB ranged between 2.78 and 298.95Mgha(-1) (average=55.8Mgha(-1)); below-ground biomass ranged between 4.06 and 436.47Mgha(-1) (average=81.47Mgha(-1)), and total carbon stock ranged between 3.22 and 345.65Mg C ha(-1) (average=64.52Mg C ha(-1)). We conclude that ALOS-2 PALSAR data can be accurately used with MLPNN models for estimating mangrove forest biomass in tropical areas.