This study proposes a multi-criteria framework for automated evaluation of scientific language quality using Large Language Models (LLMs). The approach models translation assessment as a structured decision process across semantic, grammatical, and terminological dimensions aligned with ISO 5060:2024. Validation on biomedical data shows strong agreement with expert judgment (κ = 0.81), while metric-based estimation demonstrates weak correlation. The results indicate that interpretable LLM-based evaluation can reliably replicate expert reasoning and support scalable, transparent language quality assessment.