NUSRAT JAHAN SUHA2025-11-232025-11-232021-01-20https://dspace.nstu.ac.bd/handle/123456789/123Machine translation (MT) is an automatic translation from one language to another. The benefit of machine translation is that it is possible to translate large envelop of text in a very short time. Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the possibility of a sequence of words, typically modeling entire sentences in a single integrated model. In this paper, a recurrent neural network named long short term memory is used. Overall neural Machine translation is used for translating not only Bengali languages but also different western and Asian languages to English. Unlike the traditional phrase-based translation system which consists of many small sub-part that are tuned separately, neural machine translation attempts to build and train a single, large neural network that reads a sentence and outputs a correct translation. In this model, it gives a satisfied BLEU score which quality is better than human translation. Also, a comparison between Google translator and established neural machine translation has happened. These thesis paper have also worked with 4 uncommon languages to translate into English. These languages even do not add to the Google translator. The research has also shown that western languages have given better BLEU score than the Asian language. Especially the Latin script languages have given better translation quality than other script languages. The research has worked with 27 languages. Among 27 languages 14 languages have been widely analyzed. For the poor datasets, the BLEU score is not good but in the larger datasets, it gives satisfactory results than ever. Here, the neural machine translation model is established, trained. And by using these 27 languages datasets the model translation capacity have evaluated. Established neural machine translation gives pretty good translation quality. Although all the languages have not given a higher BLEU score or accuracy like Google translator, the translation quality is good according to the BLEU score matrix algorithm. For some languages the proposed and established model has given a better BLEU score than the Google Translator. Also, some sentences of every language have compared with the output of Google translator. In this comparison, a translation difference between the established translation system and Google translator is found. For some languages, the meaning of the languages is fully reversed in Google translator. But on the other hand, the established neural machine translation system has given a good translation in these aspects. So, in this research different languages are translated to English by the Neural Machine Translation model. This can make a good contribution to the field of machine translation.