<p>Focusing on the difficulties presented by conceptual metaphors in Big Data (BD)-related literature, this research offers a novel methodology for improving English translation processing. The main goal of this study is to improve translation efficiency and accuracy via cutting-edge technologies, including machine learning, cloud computing, and big data analytics. English texts with complex metaphors are gathered and annotated for the study, and then the suggested model is compared to more conventional translation techniques. The results show that the optimized translation model performs better than traditional methods. Evaluation measures, particularly the Translation Edit Rate (TER) and Bilingual Evaluation Understudy (BLEU), show that the model records lower TER scores, which indicate fewer changes required for correctness, and higher BLEU scores, which indicate enhanced translation quality. The optimized model’s performance stabilizes as the amount of text rises, demonstrating how resilient it is to processing bigger datasets. This study shows how well the suggested model enhances translation results and illustrates how crucial it is to comprehend metaphorical language in technical situations. This research aims to give translators a better foundation for handling the difficulties of writing about Big Data by tackling the nuances of interpreting conceptual metaphors. Finally, given the quickly changing world of technology, the knowledge gathered from this study advances translation techniques and improves interlingual communication.</p>