Machine Translation (MT) refers to the process of translating text or speech from one language into another with the help of computer software, without direct human intervention. It is a significant branch of computational linguistics, artificial intelligence, and translation studies. The primary objective of machine translation is to facilitate fast, cost-effective, and large-scale translation while preserving the meaning, style, and context of the source language. In the field of *Anuvad Prakriya* (Translation Process), machine translation has emerged as a valuable technological tool that assists translators, researchers, businesses, and multilingual communication systems. The concept of machine translation originated from the idea that natural languages possess patterns and structures that can be analyzed computationally. Early machine translation systems relied on direct word-for-word substitution, but modern systems employ sophisticated linguistic rules, statistical models, and neural networks to produce more accurate and fluent translations. Popular examples include Google Translate, Microsoft Translator, and DeepL. The nature of machine translation is primarily technology-driven. It depends on algorithms, language databases, dictionaries, grammar rules, and machine learning techniques to interpret and generate translations. Modern Neural Machine Translation (NMT) systems use deep learning models that process complete sentences instead of isolated words, resulting in translations that are more natural and context-sensitive. Machine translation is automatic in nature. Once the source text is provided,