Language models have revolutionized software interactions, from simple applications like autocorrect and voice assistants to complex systems powering search engines and recommendation engines. Multilingual models, trained on more than one language, capture relationships not only within languages but also between them, enabling AI agents to understand grammar rules, word meanings, and sequence of words in a valid way. Word embeddings are a crucial component, providing vector-based representations of words that approximate the rules of language. These models have numerous applications, including document processing, where AI can extract text data from images and provide structured information. Despite limitations, multilingual models have broken barriers in machine translation, search engines, and question answering systems, with potential for further advancements.