This paper investigates machine translation between two linguistically distant languages, Arabic and Romanian, with a focus on translating from Arabic to Romanian. Dataset cleaning techniques are addressed, offering insights on the impact of translation for a language pair with limited resources. Using publicly available corpora (e.g., OPUS) and manually translated diplomatic texts, filtering methods are applied, such as duplicate removal, embedding similarity analysis (LEALLA), and Large Language Model (LLM)-based validation (Gemini-flash-002). Transformer models are trained and evaluated with diverse preprocessing pipelines that incorporate subword tokenization. Additionally, the performance of a fine-tuned LLM is assessed for this task and is compared to their pre-trained counterparts. Despite computational limitations, the results emphasize the importance of targeted preprocessing and model adaptation in improving Arabic-Romanian translation quality.