The emergence of pandemics, such as COVID-19, has demonstrated the critical need for rapid, efficient, and data-driven responses to global health crises. Traditional methods of managing pandemics often face limitations, including slow decision-making, resource shortages, and ineffective disease monitoring. Artificial Intelligence (AI) has proven to be a transformative tool in addressing these challenges, providing innovative solutions for disease surveillance, predictive modeling, resource optimization, and healthcare system support. This paper explores the pivotal role of AI in pandemic response, focusing on its applications in resource allocation, real-time disease tracking, and healthcare system efficiency. By leveraging machine learning, natural language processing, and predictive analytics, AI enhances the capacity to predict outbreaks, allocate resources effectively, and optimize healthcare workflows during a pandemic. Additionally, the paper examines the ethical and technical challenges of implementing AI, including data privacy concerns, algorithmic transparency, and the need for high-quality data. Through case studies, particularly from the COVID-19 and Ebola pandemics, the paper demonstrates AI’s effectiveness in improving public health responses. Finally, it discusses the future potential of AI in long-term pandemic preparedness and the importance of continued research, cross-sector collaboration, and investment in AI-driven health technologies. AI’s role in enhancing global pandemic resilience is both crucial and transformative, offering pathways for better crisis management and future health preparedness.