Our society is in the era of unbelievable attempts to struggle upon the spread of this life-threatening condition in terms of infrastructure, finance, business, manufacturing, and several other resources. Artificial Intelligence (AI) researchers strengthen their proficiency in developing mathematical paradigms for investigating this pandemic using nationwide distributed data. This article intends to apply the machine learning models simultaneously with the forecast of expected reachability of the COVID-19 over the nations by using the real-time data from the Johns Hopkins dashboard. Coronavirus spreads are categorized into four stages. The first stage starts with the cases recorded for the people who traveled to or from affected countries or cities, whereas in the second stage, cases are reported regionally among family, friends, and groups who came into contact with the person coming from the affected countries.

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models.