Investigating long-term water absorption (WA) and thickness swelling (TS) behaviors of wood plastic composites demand long working hours and high laboratory costs. However, using artificial intelligence methods, these behaviors can be predicted in far less time and with a low degree of error. This paper aims to predict the long-term WA and TS behaviors of a cornhusk fiber (CHF) propylene (PP) composite using the deep learning field's long short-term memory (LSTM) method. We assessed the network LSTM performance based on mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The experimental tests of WA and TS behaviors were performed on a CHF/PP composite using three different filler percentages over a period of 0–1500 h. The predictions were carried out for 200, 400, 600, 800, and 1000 h to construct a database to identify how many hours of training data are required to meet a MAPE criterion of 2% between the actual and predicted data. The results show that 200 h of training data is adequate for the LSTM method to achieve this MAPE metric. Furthermore, the metrics results validate the applicability of the proposed method. All the manufacturing metrics and codes are attached.