Improving neural machine translation (NMT) for the Mongolian-Chinese language pair is challenging due to the lack of high-quality parallel data. This study explores various noise enhancement techniques to enhance the Mongolian-Chinese Neural Machine Translation (MNMT) model’s translation quality. Techniques such as swap, token, delete, and source. Experimental results show that these methods significantly improve translation quality, with the source method yielding the most substantial enhancement. These findings indicate that noise enhancement effectively addresses data scarcity and quality issues, providing a robust strategy for improving MNMT performance.