To enable Machine Translation (MT) for low-resource languages and promote equitable access to information, leveraging the capabilities of Large Language Models (LLMs) is essential. Firstly, training LLMs on multilingual data to improve their proficiency in low-resource languages can enhance translation quality. Additionally, generating synthetic parallel data for underrepresented languages can help address the lack of training data. Collaborating with local communities and linguists to collect and annotate data in these languages can further improve translation accuracy. Moreover, developing transfer learning techniques that allow LLMs to transfer knowledge from high-resource to low-resource languages can expedite the translation process. By focusing on these strategies, LLMs can play a pivotal role in bridging the language gap and ensuring linguistic diversity in the digital space.