Transliteration is the process of converting text from one script to another while keeping phonetic and orthographic accuracy. It’s a key technique for enhancing interlingual communication and fostering accessibility in multilingual settings. The review encompasses a thorough examination of transliteration systems considering several languages and scripts. Examining their benefits, drawbacks and applications, the review classifies transliteration methods into grapheme-based, phoneme-based and hybrid systems. While phoneme-based models ensure better phonetic alignment but require significant processing power, grapheme-based approaches provide simplicity but lack in phonetic correctness. Hybrid models use statistical and deep learning techniques to integrate contextual and linguistic data, resulting in enhanced adaptability and accuracy as compared to the traditional approaches. The survey focuses on the importance of transliteration systems for Natural Language Processing (NLP), Cross-Lingual Information Retrieval (CLIR), and multilingual communication. It also addresses the challenges like inconsistency, limited data, and phonetic ambiguity. It also explores the possibilities in enhancement of current transliteration systems that will help create strong, flexible, and context-sensitive solutions to linguistic ambiguities and improve access to digital resources in multilingual setting.