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Purpose: Clostridioides difficile is the main cause of antibiotic related diarrhea and some ribotypes (RT), such as RT027, RT181 or RT078, are considered high risk clones. A fast and reliable approach for C. difficile ribotyping is needed for a correct clinical approach. This study analyses high-molecular-weight proteins for C. difficile ribotyping with MALDI-TOF MS., Methods: Sixty-nine isolates representative of the most common ribotypes in Europe were analyzed in the 17,000-65,000 m/z region and classified into 4 categories (RT027, RT181, RT078 and 'Other RTs'). Five supervised Machine Learning algorithms were tested for this purpose: K-Nearest Neighbors, Support Vector Machine, Partial Least Squares-Discriminant Analysis, Random Forest (RF) and Light-Gradient Boosting Machine (GBM)., Results: All algorithms yielded cross-validation results > 70%, being RF and Light-GBM the best performing, with 88% of agreement. Area under the ROC curve of these two algorithms was > 0.9. RT078 was correctly classified with 100% accuracy and isolates from the RT181 category could not be differentiated from RT027., Conclusions: This study shows the possibility of rapid discrimination of relevant C. difficile ribotypes by using MALDI-TOF MS. This methodology reduces the time, costs and laboriousness of current reference methods., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)