In Neural Machine Translation (NMT) with Large Language Models (LLMs), prompting has become the predominant approach for adapting to a new translation task without requiring extensive fine-tuning data. However, when translating low-resource language pairs, conventional prompts, built as simple linear text, struggle to represent richer dependency or constituency syntax, making it difficult for LLMs to grasp the source language’s syntactic patterns and semantic nuances and thus impairing translation quality. To address this challenge, this paper proposes Syntax-Aware Structured Prompting (SASP). Since word-level embeddings are insufficient for capturing the overall semantics of a sentence and are susceptible to interference from sentence length and word frequency, we encode source and candidate sentences with sentence-level embeddings and retrieve several semantically similar sentences from the target-language monolingual corpus. Subsequently, each retrieved sentence undergoes fine-grained dependency parsing to extract clause-level subject-verb-object structures as well as part-of-speech information. These syntactic patterns are then organized into clause-level structural templates and integrated with the retrieved example sentences to form a structured prompt, enhancing translation quality. We evaluate SASP on language pairs between Mongolian-Chinese (Mo-Zh), Uyghur-Chinese (Ug-Zh), and Tibetan-Chinese (Ti-Zh) using the CCMT2019 corpus. Experimental results show that SASP consistently improves translation quality across all tasks, achieving up to a 13.4% improvement over zero-shot baselines. These findings indicate that incorporating structured syntactic knowledge into prompt design can significantly enhance the performance of LLMs in low-resource machine translation, particularly in terms of syntactic accuracy and target-language consistency.