Artificial intelligence in predicting and mitigating climate-driven infectious disease outbreaks: Implications for community health resilience

As climate change accelerates the emergence, expansion and intensity of infectious disease outbreaks, conventional surveillance and modelling approaches increasingly struggle to capture the complex, non-linear interactions between environmental drivers and pathogen transmission. This review critically examines the transformative potential of artificial intelligence (AI) in predicting and mitigating climate-driven infectious diseases with particular attention to building community health resilience in vulnerable settings. Drawing on recent advances in machine learning, deep learning and hybrid modelling techniques, the analysis reveals how AI integrates multi-source data from satellite observations and meteorological records to epidemiological surveillance and human mobility patterns to deliver improved outbreak forecasts and support proactive intervention strategies for vector-borne, water-borne and zoonotic diseases. While AI demonstrates clear advantages in early warning, resource optimization and scenario simulation, significant challenges persist which include algorithmic bias, digital divides, data governance concerns and limited evidence of large-scale health impact. The review underscores that meaningful contributions to community resilience will require not only technical refinement but also equitable implementation, co-design with affected populations and deeper integration with existing health systems. Ultimately, responsibly deployed AI offers a powerful instrument for strengthening adaptive capacity and safeguarding public health amid accelerating climatic disruption.