Sanskrit, an ancient language, embodies cultural heritage, spiritual wisdom, and linguistic influence. Its depth resonates in diverse fields from Yoga to computational linguistics, fostering a global cultural exchange. Efforts to preserve Sanskrit signal a revival, affirming identity and pride worldwide. On the other hand, English is the global language of communication, commerce, and culture in the twenty-first century, fostering connections across borders and facilitating opportunities in various spheres, from business to education and innovation. Thus, the translation of these two languages unlocks their wisdom for a global audience, bridging cultural divides and preserving their relevance in contemporary contexts. The translation of these two languages presents unique challenges due to significant linguistic differences and the complexity of Sanskrit grammar. This work investigates the application of recurrent neural networks (RNNs), specifically long short-term memory (LSTM) networks, to these problems. The work of the paper goes into great detail on the LSTM model's design and training procedure.