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Machine translation is an application of natural language processing. Humans use native languages to communicate with one another, whereas programming languages communicate between humans and computers. NLP is the field that involves a broad set of techniques for analysis, manipulation and automatic generation of human languages or natural languages with the help of computers. It is essential to provide access to information to people for their development in the present information age. It is necessary to put equal emphasis on removing the barrier of language between different divisions of society. The area of NLP strives to fill this gap of the language barrier by applying machine translation. One natural language is transformed into another natural language with the aid of computers. The first few years of this area were dedicated to the development of rule-based systems. Still, later on, due to the increase in computational power, there was a transition towards statistical machine translation. The motive of machine translation is that the meaning of the translated text should be preserved during translation. This research paper aims to analyse the machine translation approaches used for resource-poor languages and determine the needs and challenges the researchers face. This paper also reviews the machine translation systems that are available for poor research languages.