Our system approach for multilingual narrative classification is basically based on XLM-RoBERTa Large and other bert-based models(e.g DeepPavlov, Neuralmind BERT), fine-tuned on different language datasets. To improve generalization and ensure robust performance across languages, we employed a repeated k-fold cross-validation strategy. This allowed us to maximize the use of available training data while mitigating potential overfitting issues. Our preprocessing pipeline included (1) language-specific tokenization, (2) hierarchical label structuring, and (3) dynamic batch sampling to balance label distributions. We optimized the model using the F1 macro and F1 samples metrics ,ensuring that the system’s predictions were well-calibrated for fine-grained multilingual classification. The results demonstrated that our approach effectively leveraged transformer-based architectures to model complex narrative structures across languages, with strong performance gains due to repeated k-fold evaluation.