The goal of this research is to use Natural Language Processing (NLP) to create a systematic and computationally sound method for recognising and categorising stop words in Sanskrit. Finding function words like conjunctions, negations, and discourse markers is essential for precise computational linguistics tasks like parsing, translation, and information retrieval because of Sanskrit's intricate morphology and syntactic structure.More than 100 high-frequency functional terms were taken from digital archives and traditional Sanskrit manuscripts. Two primary methods were employed: a statistical model that ranked and validated word frequency using Zipf's Law, and a rule-based linguistic approach based on Paninian grammar and POS tagging. To guarantee linguistic accuracy, tools like morphological analysers and Sanskrit-specific taggers were included and then expertly validated. Metrics including precision, recall, and F1-score were used for evaluation.

Both approaches produced useful but complementary outcomes. While Zipf's Law improved memory by finding statistically significant function words, the rule-based method offered great accuracy. A standardised, machine-readable list of Sanskrit stop words that is compatible with contemporary NLP processes and arranged according to grammatical functions was produced as a consequence of the hybrid approach.This work is one of the first to systematically combine statistical modelling for the categorisation of Sanskrit stop words with linguistic theory. It opens up new possibilities for machine translation, voice recognition, and semantic analysis in classical Indian languages by offering a domain-specific, verified natural language processing resource designed for an ancient language..