The proposed approach can be extended to detect machine-generated text in other languages by adapting the linguistic features and training data selection techniques to suit the characteristics of those languages. Firstly, the model can be fine-tuned on LLMs trained on different languages to capture language-specific patterns in machine-generated text. Linguistic features such as stylometry, entity grid analysis, and lexical diversity can be adjusted to account for language-specific nuances. Additionally, the training data should include a diverse range of texts in the target languages to ensure the model's generalizability.