<p>This study examines the intricate domain of machine legal translation, addressing prominent linguistic features and items of legal discourse. By gathering a corpus of legal partnership contracts released by The Jordanian Hashemite Fund for Human Development (JOHUD) organization. Using multiple translation quality metrics, including BLEU, ChrF, METEOR, fluency, and adequacy, the study adopts qualitative and quantitative analysis of the performance of three machine translators: Google Translate, ChatGPT, and Gemini, and compares their outputs to human translation. The results indicate that ChatGPT outperforms both Google Translate and Gemini across all three key metrics (BLEU, ChrF, and METEOR), demonstrating superior translation precision, requiring fewer edits, and offering better semantic accuracy. Google Translate, while slightly lagging behind ChatGPT, performs competitively, particularly in terms of fluency and adequacy. Gemini, although comparable to Google Translate in adequacy, generally underperforms in translation quality when compared to both Google Translate and ChatGPT. These findings highlight the strengths of ChatGPT in generating high-quality translations and underscore the importance of selecting machine translation systems based on the specific demands of accuracy, fluency, and meaning preservation in various applications.</p>