This study focuses on improvement of document-level neural machine translation (NMT) between English and Hindi. Traditional NMT models with K-fold cross-validation and a novel context-aware encoding system within the OpenNMT framework are used for this study. Initially, a conventional NMT model establishes a performance benchmark using the IIT Bombay parallel corpus. Then integration of a five-fold K-fold cross-validation within the OpenNMT framework is done. Further, the study introduces a custom context-aware encoder along with adaptive tuning that handles long-range dependencies and contextual nuances better than a 2-layer LSTM, essential for accurate narrative coherence in formal and literary texts. Adaptive tuning focuses on translation quality through feedback loops, optimizes English and Hindi’s complex linguistic features, and effectively manages domain-specific terms. Early results show significant enhancements over baseline models, with improvements confirmed as robust across various corpus subsets through K-fold validation. Further, the context-aware encoder with adaptive tuning gives better performance than both models and demonstrates the capability to produce translations that are not only syntactically precise but also contextually rich, setting a new standard in NMT for accuracy and relevance.