Machine translation works in language processing by using algorithms to convert text from one language to another. This process often involves Natural Language Processing (NLP) techniques, including syntax parsing and semantic analysis, to understand context and meaning. Modern machine translation systems leverage neural networks and deep learning models to produce more accurate translations by learning from vast datasets. While machine translation has improved significantly, it may still face challenges with idiomatic expressions, cultural nuances, and context-specific terminology, and human oversight may still be necessary for high-quality translations.