As a result of the COVID‐19 outbreak, which has put the world in an unprecedented predicament, thousands of people have died. Data from structured and unstructured sources are combined to create user‐friendly platforms for clinicians and researchers in an integrated bioinformatics approach. The diagnosis and treatment of COVID‐19 disease can be accelerated using AI‐based platforms. In the battle against the virus, however, researchers and decision‐makers must contend with an ever‐increasing volume of data, referred to as “big data.” VGG19 and ResNet152V2 pretrained deep learning architectures were used in this study. With these datasets, we could train and fine‐tune our model on lung ultrasound frames from healthy people as well as from patients with COVID‐19 and pneumonia. In two separate experiments, we evaluated two different classes of predictive models: one against pneumonia and the other against non‐COVID‐19. COVID‐19 can be detected and diagnosed accurately and efficiently using these models, according to the findings. Therefore, the use of these inexpensive and affordable deep learning methods should be considered as a reliable method for the diagnosis of COVID‐19.