Flue Gas Desulfurization (FGD) gypsum is generated as a byproduct for the desulfurization process, using limestone powder as an absorber in the coal-fired power plants. The FGD gypsum is high in calcium sulfate concentrations and has few impurities. Its quality is not far behind compared to natural gypsum. An artificial neural networks (ANN) study was carried out to analyze the compressive strength of the FGD gypsum mortar. The mortar mixture parameters were eight partial FGD gypsum replacements. The compressive strengths of the cured specimens were measured. The ANN model was constructed, trained and tested using these data. The data used in the ANN model was arranged in a format of input parameters that cover the cement, FGD gypsum, age of samples, and an output parameter, which is compressive strength. This study showed that the ANN can be an approach for analyzing the compressive strength of the FGD gypsum mortar using the ingredients as input parameters.