Since December 2019, the novel coronavirus (COVID-19) has been the cause of over 700,000 deaths with more than 10 million people being infected. Bangladesh, the most densely populated country in the world and now under community trans-mission of the COVID-19 outbreak. This has created huge health, social, and economic burdens. Till the 7th of August 2020, Bangladesh has reported over 250,000 infected cases and 3000 deaths. A total of 1.2 million tests has been conducted among which about 20\% of people were infected. To prevent further detriment in our scenario, predicting future consequences are very important. Previous studies have shown that machine learning(ML) models play a very significant role in forecasting the number of infections and deaths due to COVID-19. Some ML model works extremely well in providing precise information to the authorities thus enabling them to make decisions accordingly. However, Bangladesh is still lacking in this regard. To the best of our knowledge, no ML models have been applied or utilized at this point that can help in determining the pandemic circumstance for Bangladesh demographics. In this study, we explore different machine learning algorithms that can provide more accurate estimations for predicting future cases which includes infections and deaths due to COVID-19 for Bangladesh. Based on this the government, public health institutes, and policymakers can make a decision about the lockdown, management, resource mobilization, etc. Our study shows that in predicting the pandemic situations, amidst many forecasting models like Holt's Linear Regression(HLR), Support Vector Regression(SVR), Holt's Winter Additive Model(HWAM), etc. the Facebook Prophet Model(FPM) provided the best result in forecasting with remarkable precision. We believe that using this information the authorities can take decisions that will lead to the saving of countless lives of the people. Additionally, this will also help to reduce the immeasurable economic burden our country is facing due to the present status quo. On top of that our studies will help analysts to construct forecasting models for future explorations
