The purpose of this study was to use machine learning (ML) algorithms to identify tissue damage based on the mechanical outputs of computational models of spinal cord injury (SCI). Three datasets corresponding to gray matter, white matter, and the combination of gray and white matter tissues were used to train the models. These datasets were built from the comparison of histological images taken from SCI experiments in non-human primates and corresponding subject-specific finite element (FE) models. Four ML algorithms were evaluated and compared using cross-validation and the area under the receiver operating characteristic curve (AUC). After hyperparameter tuning, the AUC mean values for the algorithms ranged between 0.79 and 0.82, with a standard deviation no greater than 0.02. The findings of this study also showed that k-nearest neighbors and logistic regression algorithms were better at identifying injured elements than support vector machines and decision trees. Additionally, depending on the evaluated dataset, the mean values of other performance metrics, such as precision and recall, varied between algorithms. These initial results suggest that different algorithms might be more sensitive to the skewed distribution of classes in the studied datasets, and that identifying damage independently or simultaneously in the gray and white matter tissues might require a better definition of relevant features and the use of different ML algorithms. These approaches will contribute to improving the current understanding of the relationship between mechanical loading and tissue damage during SCI and will have implications for the development of prevention strategies for this condition.Clinical Relevance- Linking FE model predictions of mechanical loading to tissue damage is an essential step for FE models to provide clinically relevant information. Combined with imaging technologies, these models can provide useful insights to predict the extent of damage in animal subjects and guide the decision-making process during treatment planning.