Due to a lack of parallel data, low-resource language machine translation has been unable to make the most of Neural Machine Translation. This paper investigates several approaches as to how low-resource Neural Machine Translation can be improved in a strictly low-resource setting, especially for bidirectional Khasi-English language pairs. The back-translation method is used to expand the parallel corpus using monolingual data. The work also experimented with subword tokenizers to improve the translation accuracy for new and rare words. Transformer, a cutting-edge NMT model, serves as the backbone of the bidirectional Khasi-English machine translation. The final Khasi-to-English and English-to-Khasi NMT models trained using both authentic and synthetic parallel corpora show an increase of 2.34 and 3.1 BLEU scores, respectively, when compared to the models trained using only authentic parallel dataset.