The purpose of this paper is to see how Machine Learning (ML) algorithms and applications are used in the COVID-19 inquiry and for other purposes. The available traditional methods for COVID-19 international epidemic prediction, researchers and authorities have given more attention to simple statistical and epidemiological methodologies. The inadequacy and absence of medical testing for diagnosing and identifying a solution is one of the key challenges in preventing the spread of COVID-19. A few statistical-based improvements are being strengthened to answer this challenge, resulting in a partial resolution up to a certain level. ML have advocated a wide range of intelligence-based approaches, frameworks, and equipment to cope with the issues of the medical industry. The application of inventive structure such as Machine Learning and other in handling COVID-19 relevant outbreak difficulties has been investigated in this work. The major goal of this research is to (i) Examining the impact of the data type and data nature, as well as obstacles in data processing for COVID-19. (ii) Better grasp the importance of intelligent approaches like machine learning for the COVID-19 pandemic. (iii) The development of improved Machine Learning algorithms and types of Machine Learning for COVID-19 prognosis. (iv) Examining the effectiveness and influence of various strategies in COVID-19 pandemic. (v) To target on certain potential issues in COVID-19 diagnosis in order to motivate academics to innovate and expand their knowledge and research into additional COVID-19-affected industries
