Transforming text from one language to another by using computer systems automatically or with little human interventions is known as Machine Translation System (MTS). Divergence among natural languages in a multilingual environment makes Machine Translation (MT) a difficult and challenging task. The purpose of this paper is to present a comprehensive survey of MTS in general and for English, Hindi and Sanskrit languages in particular. The state-of-the-art MT approach is Neural Machine Translation (NMT) which has been used by Google, Amazon, Facebook and Microsoft but it requires large corpus as well as high computing systems. The availability of MT language modeling tools, parsers data repositories and evaluation metrics has been tabulated in this article. The classification of MTS, evaluation methods and platforms has been done based on a well-defined set of criteria. The new research avenues have been explored in this survey article which will help in developing good quality MTS. Although several surveys have been done on MTS but none of them have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach including tools and evaluation methods as done in this survey specifically for English, Hindi and Sanskrit languages.