BACKGROUND In the modern world, mobile applications are essential to human advancement and pandemic control is no exception. The use of mobile applications and technology for the detection and diagnosis of COVID-19 disease has been the subject of numerous investigations. Objective: Since no thorough analysis of the COVID-19 epidemic prevention has been done using mobile applications. Due to this gap, the current study thoroughly covers the many uses of mobile applications for COVID-19 detection and diagnosis. OBJECTIVE Since no thorough analysis of the COVID-19 epidemic prevention has been done using mobile applications. Due to this gap, the current study thoroughly covers the many uses of mobile applications for COVID-19 detection and diagnosis. METHODS A search of five major research databases (ScienceDirect, Scopus, PubMed, Web of Science, IEEE) found 535 studies, among which 42 related to the diagnosis and detection of patients suspected of having COVID-19. RESULTS Mobile applications can be categorized into five areas based on the content of these studies: contact tracing, data gathering, data visualization, artificial intelligence-based methods, rule- and guideline-based methods, and data transformation. Patients with COVID-19 have been identified using mobile applications employing a variety of clinical, geographic, demographic, radiological, serological, and laboratory data. The majority of studies concentrated on using artificial intelligence (AI) methods to identify people who might have COVID-19. Additionally, compared to other data types, symptoms, cough sounds, and radiological images were used more frequently. CONCLUSIONS Mobile applications could soon play a significant role as a powerful tool for data collection, epidemic health data analysis, and early identification of suspected cases. These technologies can work in conjunction with the Internet of Things (IoT), cloud storage, 5G, and cloud computing.