Machine learning methods have been widely used in the area of solid mechanics due to the large volume of data available in the literature. The motivation for this work was the study of the accumulation of plastic strain at the grain scale. Because the use of machine learning can be a significant contribution to creating models capable of predicting the accumulation of deformation. The objective of this work was to improve the prediction of plastic strain accumulation by proposing a new method for predicting the accumulation of plastic strains in grain boundaries of a polycrystalline material, using machine learning models. This work uses experimental data from the literature to structure three databases, which only consider grain boundaries. The following methods were used in the predictions: Decision Tree, Random Forest, Stochastic Gradient Descent, K-Nearest Neighbors, Gradient Boosting Regressor, and Principal Component Analysis (PCA). Monte Carlo crossvalidation and resampling methods were used to evaluate the models. The error metrics applied were the coefficient of determination (R2) and the Pearson correlation coefficient (R). The results indicate that the predictions were coherent and of good quality, improving the average Pearson coefficient values by approximately 30 percent compared to literature values. For R(2), the average value achieved was 0.85. It is concluded that the use of the machine learning method proves to be reliable in predicting the accumulation of plastic strain at the grain boundary of a polycrystalline material.