The globe is more technologically, socially, and culturally united when languages are used. Interlanguage information translation is essential for the interchange of ideas and information because different native speakers speak different languages. Even though Sanskrit is an old Indo-European language, it still requires a lot of information processing work to be thoroughly studied and utilized to open up new possibilities in computer science and computational languages. This paper describes a machine translation system that can convert Sanskrit to Hindi. The presented method uses a multi-head attention mechanism to train a transformer-based neural machine translation system. This is a novel strategy that can be applied to any low-resource language with rich morphology. It is a multi-field, universal system with minimal need for human interaction. Additionally, we constructed a parallel corpus for the language pair Sanskrit and Hindi. The corpus now contains more than fifty thousand parallel sentences. The BLEU performance metric was used to automatically assess the system. With an automatic assessment metrics-derived BLEU Score of 67.8%, the proposed approach outperformed the existing solutions. The results show that the developed and recommended strategy outperforms earlier studies for this language combination.