The present study showed that machine learning can be used to predict the tensile shear strength of bonded beech wood as a function of the composition of polyurethane prepolymers and various pretreatments. A comprehensive experimental data set was used for training and testing the algorithms support vector machines, random forest and artificial neural networks. Within the framework of the experiments, the structure-property relationships of 1C PUR prepolymers were analyzed by systematical variation of the structural parameters urea and urethane group content, cross-link density, ethylene oxide content, and the functionality via isocyanate (NCO) or polyether component. The bonded wood joints were tested according to DIN EN 302-1. Prior to testing, the shear test specimens were pretreated according to procedures A1 and A4, five temperature steps (5, 40, 70, 150 and 200 °C) and two alternating climates. The complete data set (N = 2840) was preprocessed and split into a training set and a test set using tenfold cross-validation. The performance of the algorithms was evaluated with the coefficient of determination (R²), root-mean-square error (RMSE) and mean absolute percentage error (MAPE). All machine learning algorithms revealed a high accuracy, but the artificial neural network showed the best performance with R²= 0.92, RMSE = 0.948 and a MAPE of 9.21. The work paves the way for future machine learning applications in the field of adhesive bonding technology and may enable a fast and effective development of new adhesives and enhance the efficiency of their application.