Climate change is increasingly reshaping agricultural ecosystems, creating favorable conditions for the emergence, spread, and evolution of plant pathogens that threaten global food security. Rising temperatures, altered precipitation patterns, elevated atmospheric CO₂ levels, and extreme weather events significantly influence host–pathogen interactions, pathogen life cycles, and disease epidemiology. Traditional approaches to plant disease monitoring and management often struggle to keep pace with these rapid, climate-driven changes. In this context, Artificial Intelligence (AI) and its subset, Machine Learning (ML), have emerged as powerful tools for understanding, predicting, and managing climate-induced plant disease risks. This study explores the role of AI-driven approaches in analyzing large-scale climatic, genomic, phenotypic, and remote-sensing datasets to detect disease patterns, forecast pathogen outbreaks, and support climate-resilient agricultural decision-making. By integrating supervised, unsupervised, and deep learning techniques with data from sensors, drones, and high-throughput phenotyping platforms, AI enables early disease detection, accurate prediction of pathogen emergence, and targeted intervention strategies. The application of AI in plant pathology not only enhances predictive accuracy under changing climatic conditions but also supports sustainable crop protection, efficient resource use, and improved resilience of agro-ecosystems. Overall, this work highlights AI as a transformative approach to addressing the complex challenges posed by climate change on plant pathogen emergence and agricultural sustainability.