Machine translation works by utilizing natural language processing (NLP) techniques to convert text from one language to another. The process generally involves two main approaches: rule-based and statistical/machine learning. Rule-based systems rely on pre-defined linguistic rules and dictionaries, while statistical methods analyze vast bilingual corpora to determine the most likely translations. Modern machine translation systems often use neural networks to better capture context and nuances in language, significantly improving translation quality. The goal is to retain the original message's meaning while making it grammatically correct in the target language.