While the NMT system operates conventional techniques such as rule-based machine translation and statistical machine translation, the manual human translation still falls short. Our two NMT systems, RNN sequence-to-sequence, and transformer-based models are used in this paper for English-to-Hindi translation and are compared to the current MT output for the BLEU score. It outperforms current performance systems. However, a thorough review of the translations projected shows that in instances when an unknown word is recognized, blank lines emerge in the output and the source phrase is translated in a number of ways, our NMT systems need to be improved. In addition, the finding of the effect of the bi-gram model on the Hindi translation and relation between comparable Indian languages provides a new research route for direct translation between couples of similar languages. It may be possible to circumvent the limitation of available parallel data in low-resource languages by using linguistic similarities to get accurate results. With English to Hindi, an LSTM-based care mechanism enhances the MT output of the GRU-based NMT system. We also evaluated MT output performance in the Indian language, Hindi, using the BLEU-1, BLEU-2, and BLEU3 scores. For an Indian language like Hindi, it has been pointed out that it is not sufficient to assess on the basis of the BLEU1 score, as in prior research. In any configuration of NMT systems, the average BLEU score obtained is close to the matching bi-gram BLEU score.