Abstract: Digital translation (MT) is a process of translating text from one language to another using software by including both computer and language information. Initially, the MT system acquires text translation into the source language by simply associating the meaning of the words in the source language with the target language with the help of grammar. However, such methods did not produce good results due to their failure to capture the various sentence structures in the language. This process of translation is time-consuming and requires skilled craftsmen in both languages. Subsequently, integrated translation methods such as mathematical translation (SMT) and neural translation (NMT) technology have been introduced to address the challenges of legal-based approaches. MT has already shown promising results in bilingual translation. In contrast to SMT, which requires sub-components trained separately in translation, NMT uses one large neural network for training. This paper outlines how to train a repetitive neural network for rearrangement for a source to identify. The default encoder helps to reconstruct the vectors of the target language. Therefore, powerful hardware (GPU) support is required. The GPU improves system performance by reducing training time.