In this work, a new natural language processing system that can translate English sentences into Telugu and anticipate verb tenses is presented. This system provides an integrated solution for language learners and educators by integrating a translation model (mBART), and a rule- based approach for tense conversion. The rule-based approach efficiently applies the grammar rules for tense conversion by using an extensive dataset of verb forms. To accurately apply auxiliary verbs and appropriate verb conjugations depending on user-specified tenses, SpaCy is used to detect subjects and verbs within sentences. This method provides accurate tense prediction by utilizing linguistic concepts. The mBART translation model, which is further improved by pre-trained techniques, is integrated into the system to improve the quality of the translation. These methods improve the general fluency and accuracy of the translated Telugu sentences by addressing popular phrases and grammatical structures. The hybrid approach provides educators and language learners with a useful tool by fusing computational techniques with linguistic concepts. It is a useful tool in many NLP applications since it offers a precise and effective way to comprehend and modify language.