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This study investigates the pivotal role of artificial intelligence in the development of translation, with a particular focus on neural translation, which relies on algorithms and deep networks to interpret texts within their full context. It examines the translation of linguistic terminology, considered one of the most challenging domains of translation due to its demand for precision and a deep awareness of specialized concepts. The study adopts an applied methodology that combines theoretical analysis with a practical comparison between human translation outputs and those produced by AI-based systems (such as Google Translate, DeepL, and ChatGPT). Using a sample of linguistic terms drawn from both Arabic and foreign academic texts, the findings reveal that artificial intelligence can enhance translation quality, reduce errors, and increase efficiency, provided it is trained on rich and diverse Arabic corpora. The study concludes that the role of the human translator must be integrated with the capabilities of intelligent systems to ensure accurate translation that preserves meaning and supports the dissemination of the Arabic language in the global digital space.