The Romanized text has become popu lar with the growth of digital communi cation platforms, largely due to the fa miliarity with English keyboards. In Sri Lanka, Romanized Sinhala, commonly re ferred to as “Singlish” is widely used in digi tal communications. This paper introduces a novel context-aware back-transliteration system designed to address the ad-hoc typ ing patterns and lexical ambiguity inher ent in Singlish. The proposed system com bines dictionary-based mapping for Singlish words, a rule-based transliteration for out of-vocabulary words and a BERT-based language model for addressing lexical am biguities. Evaluation results demonstrate the robustness of the proposed approach, achieving high BLEU scores along with low Word Error Rate (WER) and Character Er ror Rate (CER) across test datasets. This study provides an effective solution for Ro manized Sinhala back-transliteration and establishes the foundation for improving NLP tools for similar low-resourced lan guages.