The rapid diagnosis of COVID-19 has become a pressing issue due to the strain the outbreak has placed on the healthcare system. This article aims to investigate the rapid and accurate diagnosis of COVID-19. This paper first introduces several widely used COVID-19 diagnostic techniques: rRT-PCR has excellent specificity and sensitivity, making it one of the most trustworthy ways to find the SARS-CoV-2 virus. Diagnostics based on X-rays are frequently employed as an adjunctive method. CT-based diagnosis can offer comprehensive details regarding lung health. It then highlights how machine learning combined with X-ray and CT images can be used to diagnose COVID-19. This approach can improve the accuracy and efficiency of detecting and evaluating the disease, helping healthcare professionals make decisions. Several standard machine learning methods are introduced, including supervised, unsupervised, and semi-supervised learning. Lastly, it forecasts machine learning development in the healthcare sector.