Identifying wild bird species associated with highly pathogenic avian influenza (HPAI) is essential for optimizing surveillance and mitigating spillover risks. This study analyzes Brazil´s nationwide HPAI surveillance data (up to July 2025), comprising 1,153 records across 127 bird and mammal species from 525 municipalities. Using a multi-model framework-including chi-square association tests, binary logistic regression, and spatial Generalized Additive Models (GAMs). Species-outbreak associations were significative and positive for Thalasseus maximus (χ2 = 237.34, p < 0.0001), Thalasseus acuflavidus (χ2 = 216.12, p < 0.0001), Sterna hirundo (χ2 = 83.88, p < 0.0001), and Sterna hirundinacea (χ2 = 77.56, p < 0.0001). An optimized logistic regression model (model 3) highlighted T. acuflavidus as the strongest predictor of HPAI (OR = 80.74; 95% CI: 21.85-298.39; p < 0.001), achieving good predictive performance (AUC = 0.85; Pseudo R2 = 0.48). To account for spatial dependence, we fit a binomial GAM incorporating a bivariate longitude-latitude spatial smoother, significantly improving model fit (AUC = 0.96; Pseudo R2 = 0.58) and effectively accounting for residual spatial autocorrelation (Moran´s I = 0.0399, z = 2.10, p = 0.044). Outbreaks were concentrated along Brazil´s southeastern coast, overlapping with high-density poultry zones, while inland spread remained sporadic, suggesting migratory routes as key transmission pathways. These results underscore the critical role of seabirds-particularly T. acuflavidus-in HPAI H5N1 dynamics in Brazil. The enhanced predictive power of the spatial GAM supports its utility in risk mapping. We recommend integrating biodiversity data with spatial modeling to guide targeted surveillance in high-risk coastal areas, reducing spillover threats to poultry and wild populations.