The world has been significantly impacted by the global pandemic of COVID-19, leading researchers to explore various methods for detecting the virus. Deep learning (DL) technologies have emerged as pivotal tools in detecting and managing the virus. This review article aims to provide a comprehensive examination of the advancements and applications of DL in the context of COVID-19 detection, offering insights into the evolution, impact, and future direction of this rapidly evolving field. A thorough bibliometric review was carried out using the Scopus database. The methodology involved keyword-based searches, analyses of publication trends, and studies of co-citation networks, with a focus on literature from 2020 to 2023. Data visualisation tools, particularly VOSviewer, were used to analyse and map bibliometric data, highlighting publication trends, and authorship patterns. The study found a substantial rise in DL research for detecting COVID-19 from 2020 to 2023, with significant input from countries such as India, China, and Saudi Arabia. The trends in research are expected to showcase developments in DL models for medical imaging, including CT scans and X-rays, as well as the increasing significance of AI in medical diagnostics. The study also pinpointed the main collaborative networks and popular keywords in this research area. This bibliometric analysis aims to lay the foundation for future research directions by offering a comprehensive overview of the evolution of DL in COVID-19 detection It will provide strategic insights and research directions to advance this vital domain at the intersection of computational intelligence and global health.