The main challenge of machine translation technology is achieving high-quality translations that accurately convey meaning, tone, and context. While advancements in AI and neural networks have improved translation accuracy, nuances, idiomatic expressions, and cultural references often remain difficult to translate effectively. Automatic translation tools may struggle with complex sentence structures or specialized terminology, leading to errors or misinterpretations. Ensuring fluency and coherence in translated text, alongside handling diverse languages, presents ongoing research and development challenges for improving machine translation systems' reliability and effectiveness.