One of the fundamental issues in natural language processing (NLP) is anaphora resolution, which is matching pronouns such as ‘he’, ‘she’, ‘it’, or ‘they’ to the right antecedents or referents in a text. Finding the words or phrases in the text that these pronouns are referring to is the main objective of anaphora resolution. Pronouns are frequently ambiguous, and several words or phrases in the text may potentially act as their antecedents, making this problem difficult to solve. To properly assign each pronoun to the appropriate referent, anaphora resolution necessitates taking the text’s semantics and context into account. Many NLP applications, such as machine translation, text summarization, question answering, and co-reference analysis, depend on successful anaphora resolution. The results demonstrate the effectiveness of our proposed methodology in resolving anaphoric references in Magahi texts.