The subject of language translation has seen a substantial transformation because of Artificial Intelligence (AI), particularly in multilingual nations like India where linguistic diversity poses a special set of difficulties. The use of AI in Indian language translation is examined in this work, with a particular emphasis on neural and statistical techniques. It draws attention to the increasing requirement for translation systems in India because of the country’s linguistic diversity and the vast majority of people’s poor English language skills. Neural machine translation (NMT), in particular, is an AI-driven approach that shows great promise for bridging language barriers in regional communication. Rule-Based Machine Translation (RBMT), Statistical Machine Translation (SMT), and Neural Machine Translation (NMT) are three of the AI-based translation methods that are examined in this research. It also tackles important issues including the lack of data, especially for low-resource languages, and the requirement for domain-specific models. According to the study, there are still obstacles in the way of completely realizing AI’s enormous promise for enhancing translation accuracy, managing linguistic complexity, and promoting cross-cultural communication.