Due to the ease of access and the exponential expansion of information on social media networks, it is impossible to distinguish between bogus and authentic information. The easy dissemination of knowledge through sharing has contributed to the explosive growth of information fraud. The legitimacy of social media networks is also in jeopardy in areas where the dissemination of incorrect information is pervasive. As such, automatically classifying material as true or false based on its publisher, source, and content has emerged as a research challenge. Machine learning has proven invaluable in data classification, even with its limitations. This study looks at different machine learning strategies for spotting false news, and it summarizes both the novel ways that researchers have proposed and the approaches that are currently in use.