<![CDATA[The increasing need for cross-lingual communication in a globalized world has driven innovations in machine translation, with the aim of breaking down language barriers. Traditional machine translation models rely on large parallel corpora for training, but zero-shot machine translation (MT) offers a promising alternative by enabling translation between language pairs without requiring direct training data. This study explores the potential of generative AI in zero-shot MT, focusing on its ability to bridge languages with minimal linguistic resources. The research aims to evaluate the effectiveness of zero-shot MT using generative AI models in translating between languages that lack extensive parallel corpora. The study uses a comparative approach, assessing the performance of generative AI models against traditional MT systems on several language pairs, particularly focusing on low-resource languages. The results show that generative AI-based zero-shot models can produce high-quality translations across multiple languages, even those with limited or no parallel data. The findings suggest that zero-shot MT has the potential to revolutionize global communication by offering a scalable, efficient solution for languages with insufficient resources. This research concludes that generative AI's ability to perform zero-shot machine translation has significant implications for improving cross-cultural communication and expanding access to information, with important applications in education, business, and diplomacy.]]>
