A Comprehensive Review on the Application of Deep Learning Techniques for COVID‑19 Diagnosis, Prediction, and Management

Abstract

The COVID‑19 pandemic has profoundly impacted global health systems and economies, prompting the urgent need for data driven solutions. Machine Learning (ML), a subset of Artificial Intelligence (AI), has demonstrated significant potential in the diagnosis, prediction, and management of COVID‑19. This review provides a comprehensive analysis of ML techniques applied across various domains, including medical imaging, epidemiological forecasting, and clinical data analytics. Recent advances in deep learning architectures, such as Convolutional Neural Networks (CNNs), Long Short‑Term Memory (LSTM) networks, and hybrid frameworks, have achieved remarkable results in COVID‑19 detection and forecasting tasks. The paper also examines data challenges, model performance comparisons, interpretability issues, and open research directions. By consolidating over 200 studies, this work identifies the strengths, limitations, and emerging trends shaping the future of AI‑driven pandemic intelligence.