Moisture is one of the most important factors impacting the talc pellet process. In this study, a hybrid model (HM) based on the combination of intelligent algorithms, self-organizing map (SOM), the adaptive neuron fuzzy inference system (ANFIS) and metaheuristic optimizations, genetic algorithm (GA) and particle swarm optimization (PSO) is introduced, namely, HM-GA and HM-PSO. The main purpose is to predict the moisture in the talc pellet process related to symmetry in the aspect of real-world application problem. In the combination process, SOM classifies the suitable input data. The GA and PSO, as the training algorithms of ANFIS, are investigated to compare the prediction skill. Five factors, including talc powder, water, temperature, feed speed, and air flow of 52 experiment cases designed by central composite design (CCD), are the training set data. Three different measures evaluate the capacity of moisture prediction. The comparison results show that the HM-PSO can provide the smallest difference between train and test datasets under the condition of the moisture being less than 5%. As a result, the HM-PSO model achieves the best result in predicting the moisture for the talc pellet process with R = 0.9539, RMSE = 1.0693, and AAD = 0.393, compared to others.