Avian influenza viruses (AIV) have a remarkable capacity to adapt through rapid mutation and cross-species transmission, posing a major global pandemic threat. The distinct spatial patterns of outbreaks suggest that the relationship between disease emergence and ecological drivers is complex, non-linear, and varies with virus pathogenicity and host characteristics. Here, we used a multi-algorithm machine learning statistical framework and 27 ecological features to analyze over 50,000 outbreaks reported across Eurasia and Africa from 2004 to 2025, stratifying the data into highly pathogenic (HPAI), low-pathogenic (LPAI), domestic, and wild-bird subsets. Our models achieved high predictive performance (accuracies?≥?84%) and revealed distinct, strata-specific risk profiles driven by strong non-linear interactions. For the all-AIV and wild-bird cohorts, vegetation indices and wetland proximity were the most important predictors of spatial risk. Conversely, domestic duck and chicken densities dominated predictions for the LPAI and domestic poultry subsystems, while proximity to LPAI outbreaks was the strongest driver of HPAI risk. Although underreporting in wildlife and developing regions limits data quality, this interpretable framework provides a flexible platform to support risk-based surveillance and to optimize targeted interventions at regional and global scales.