This study presents an effective method based on long short-term memory to reduce the computational cost in nonlinear static analysis of functionally graded plates. Data points representing a load-deflection curve in a dataset are generated through isogeometric analysis (IGA). The order of these data points is always maintained as a sequential series of observations; therefore, it is referred to as a time series. Dataset is divided into three sets including training, testing, and prediction sets. Both training and testing sets are used for the training process by the long short-term memory to gain optimum weights. Based on these obtained weights, data points in the prediction set are directly predicted without using any analysis tools. The effectiveness and accuracy of the proposed method are demonstrated by comparing the obtained results to those of isogeometric analysis.