A dramatic increase in the number of instances of any disease invariably creates concern among common people. COVID-19 is a recent outbreak that has troubled the world. Due to its high fatality and dissemination rates, people were terrified of getting infected with it. This covid phobia has prompted individuals to misdiagnose themselves due to symptom similarities with other popular diseases viz., common cold, flu, and allergy. Thus, this study introduces a machine learning-driven approach to discern whether an individual presenting as a suspect is genuinely infected with COVID-19 or possibly affected by other common diseases. On evaluating multiple machine learning models, the Naive Bayes (NB) model exhibited superior performance with an accuracy of 95.41% for precise identification. As a result, this research can be used to differentiate between COVID-19 and other common diseases that have similar symptoms. Future studies can widen the scope to include more diseases having COVID-19-like symptoms.