The integration of artificial intelligence (AI) into public health preparedness offers a transformative pathway to strengthening system resilience, enhancing early warning capabilities, and improving response capacity to emerging and ongoing health threats. Traditional public health surveillance and emergency response frameworks often rely on delayed reporting, fragmented data sources, and resource-intensive analytical processes, which can limit timely decision-making. AI addresses these constraints by enabling rapid analysis of large, heterogeneous datasets, including epidemiological records, mobility data, environmental indicators, and digital health signals. Machine learning and predictive analytics support earlier detection of outbreaks, more accurate risk stratification, and scenario modelling to anticipate healthcare system strain. In preparedness planning, AI-driven tools enhance resource allocation, workforce planning, and supply chain resilience, while decision-support systems assist policymakers during crisis response by synthesising real-time evidence. However, effective integration requires robust data governance, interoperability across health systems, transparency of algorithms, and attention to ethical concerns such as bias, privacy, and accountability. This paper examines how AI can be systematically embedded within public health preparedness architectures to complement human expertise, strengthen adaptive capacity, and support proactive rather than reactive responses to health emergencies. By aligning technological innovation with governance and equity considerations, AI can play a critical role in building resilient public health systems capable of responding to complex and evolving risks.