For large language models (LLMs) like NLLB and GPT, translating idioms remains a challenge as the non-computational nature of idioms may cause traditional Transformer-based systems to translate idioms literally, failing to convey the proper meaning. Previous work has utilized knowledge bases like IdiomKB by providing the LLM with the meaning of an idiom to use in translation. Although this method yielded better results than a direct translation, it is still limited in its ability to preserve idiomatic writing style across languages. Our goal is to enhance translation fidelity by improving LLM processing of idiomatic language while preserving the original linguistic style, ensuring translated texts retain their cultural nuances and emotional resonance. In this research, we expand upon the knowledge base to find corresponding idioms in the target language. We benchmark two methods: The first method employs the SentenceTransformers model to semantically generate cosine similarity scores between the meanings of the original and target language idioms, selecting the best idiom (Semantic Idiom Alignment method, or SIA). The second method uses an LLM to find a corresponding idiom in the target language for use in the translation (LLM-based Idiom Alignment method, or LIA). As a baseline, we performed a direct translation without providing additional information. Human evaluations on the English -> Chinese, Chinese -> English, English -> Urdu, and Hindi -> English show the SIA method outperformed others in all GPT4o translations. To further build upon IdiomKB, we developed a low-resource Urdu dataset and a Hindi dataset containing idioms and their translations. Despite dataset limitations, the SIA method and LIA method show promise, potentially overcoming language barriers and enabling the exploration of diverse literary works in Chinese, Urdu, and Hindi.