Machine Translation has seen rapid advancements due to progress in neural sciences and Large Language Models. While machine translation remains highly competitive across many language pairs, especially those that are linguistically and culturally close like English and Spanish (Moslem et al., 2023), the situation between English and Arabic requires further exploration. This study investigates the performance of Neural Machine Translation (represented by Google Translate), Large Language Models (represented by ChatGPT), and human translation in translating English texts into Arabic across four genres: general, literary, scientific, and media. Human evaluations were used to measure translation quality based on accuracy, fluency, style, cultural fit, and terminology. The results indicate that human translation is the most accurate, especially in capturing cultural and contextual nuances. ChatGPT, when used with detailed prompts, often outperforms both Google Translate and ChatGPT with simple prompts, particularly in literary and media genres. On the contrary, Google Translate performs the worst overall, especially with scientific and general texts, due to issues like word confusion and cultural inaccuracies. These results offer practical insights for translation educators, professionals, and students on effectively integrating Machine Translation tools while appreciating the irreplaceable value of human translators.