The COVID-19 pandemic triggered the urge to utilize machine learning to build models to predict biomedical waste for futuristic waste management. In this study, the machine learning approach (Random Forest Regressor) is used to build a model that predicts biomedical waste weight generated based on the COVID-19 pandemic case. Data gathered during the period of COVID-19, which is specific to the United Arab Emirates and key features of the study were involved in the machine learning algorithm (PCR tests, vaccine doses, Masks and Death Wraps). The generated model showed 92.67% accuracy, reflecting the overall predictive accuracy relative to the average biomedical waste observed. Pareto chart is used to identify which feature affects the overall generated biomedical waste; in this case, the model's initial prediction for a hypothetical scenario showed that around 2015.53 kg of the total biomedical waste is generated from PCR tests, whereas the least contributor was due to death wraps around 41.13 kilograms. The model can predict a COVID-19 case like a pandemic, with futuristic model improvements to include other vital features to the algorithms of the model.