Neural Machine Translation (NMT) is a state-of-the-art approach in the field of machine translation that employs neural networks to translate text from one language to another. Unlike traditional statistical methods that rely on phrase-based translation, NMT models leverage deep learning techniques to understand and generate text in a more context-aware manner. By training on large datasets, NMT systems can learn the nuances of languages, including grammar, syntax, and idiomatic expressions. This leads to improved translation quality and fluency, making NMT the preferred choice for modern translation applications. Major advancements in NMT have been facilitated by architectures like the Transformer, which enables efficient training and superior performance on various translation tasks.