Aqueous hydrolysis is used to chemically recycle polyethylene terephthalate (PET) due to production of high-quality terephthalic acid (TPA), the PET monomer. PET hydrolysis depends on various factors including PET size, catalyst concentration, and reaction temperature. So, modeling PET hydrolysis by considering the effective factors can provide useful information for material researchers to specify how to design and run these reactions. It will save time, energy, and materials by optimizing the hydrolysis conditions. Machine learning algorithms enable to design models to predict the output results. For the first time, 381 experimental data were gathered to model aqueous hydrolysis of PET. Effective factors on PET hydrolysis were connected to the TPA yield. The logistic regression was applied to rank the effective factors. Two algorithms were proposed, artificial neural network multi-layer perceptron (ANN-MLP) and adaptive network-based fuzzy inference system (ANFIS). The dataset was divided into training, validating, and testing sets to train, validate, and test the models, respectively. The models predicted TPA yield sufficiently where the ANFIS model outperformed. R-squared (R2) and Root Mean Square Error (RMSE) loss functions were employed to measure the efficiency of the models and evaluate their performance.