Predictive maintenance becomes crucial nowadays in industry 4.0 since it will have a high impact on the industrial economy. Therefore, accurate predictive maintenance growing high demand for handling the failure of big plants effectively. In this paper, the model of predictive maintenance for Air Booster Compressor (ABC) Motor failure is using Artificial Neural Network (ANN) is presented. However, the optimal weights of the network are one of the parameters that lead to the accuracy of ANN. Therefore, Particle Swarm Optimization (PSO) is proposed to train the weights and bias of ANN. The result presented in this paper is compared with conventional ANN based on Mean Square Error (MSE) and Root Mean Square Error (RMSE) © 2019 IEEE.