Machine Transliteration is the process of automated conversions of words from source language to target language while preserving pronunciation. It plays a crucial role in bridging language barriers by enabling seamless script conversions and enhancing cross-linguistic communication. Over time, various transliteration approaches have been developed like Ruled Based, Hidden Markov Model, and statistical approach. Rule based is one of the earliest approaches, which relies on predefined linguistic rules and structured mappings to achieve accurate script conversion. This study explores the variations of rule-based transliteration systems, analyzing their challenges, effectiveness and working. Understanding these systems is vital for improving language accessibility and enhancing global digital communication.