This review explores the innovative applications of artificial intelligence (AI) during the COVID-19 pandemic. AI technologies have shown great potential in addressing the global challenges posed by the pandemic. The review discusses the impacts of the pandemic on public health, the economy, and society, and highlights the innovative applications of advanced AI technologies in key areas such as pandemic prediction, detection, control, and drug discovery. AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. High-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. The review also discusses future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Although promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.This review explores the innovative applications of artificial intelligence (AI) during the COVID-19 pandemic. AI technologies have shown great potential in addressing the global challenges posed by the pandemic. The review discusses the impacts of the pandemic on public health, the economy, and society, and highlights the innovative applications of advanced AI technologies in key areas such as pandemic prediction, detection, control, and drug discovery. AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. High-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. The review also discusses future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Although promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.