Abstract: With escalating global challenges in biodiversity conservation, the integration of Artificial Intelligence (AI) technologies has emerged as a critical area of ecological advancement. This synergy shows significant potential in key areas such as conservation data analytics, multispecies decision-making frameworks, multimodal data applications, species identification systems, and big data integration. Using bibliometric analysis of the Web of Science (WoS) and China National Knowledge Infrastructure (CNKI) databases spanning 2005-2025, this study investigates the evolving patterns of AI applications in biodiversity research. We systematically trace the development of AI-ecology integration in conservation practices, identify cutting-edge innovations and persistent challenges, and propose future research directions. The results show that: (1) The number of publications has increased 33-fold since 2005, with China becoming the leading contributor to English-language research and maintaining a central role in international scientific collaborations. (2) Global research focuses on three major clusters including conservation decision-making systems, ecological mechanism modeling, and AI technological innovation, particularly in global-scale predictive modeling and multimodal data fusion. Chinese research excels in intelligent monitoring systems, agroecological applications, and data-driven decision frameworks. (3) Thematic development has progressed through three distinct phases including foundational theoretical exploration (2005-2015), technological advancement with multimodal integration (2016-2020) and global intelligent implementation (since 2021). (4) Research frontiers have evolved from basic species recognition to include automated monitoring networks, multimodal data synthesis, ecological process simulation, and intelligent decision optimization. Ongoing challenges include mitigating data bias, resolving multi-source heterogeneity, and addressing trade-offs in multi-objective conservation. We recommend developing multimodal biodiversity datasets with cross-species conservation mechanisms, advancing cross-scale ecological modeling and edge intelligence systems, creating next-generation AI architectures that integrate biodiversity knowledge graphs, promoting localized large-model development in conjunction with global governance frameworks, and deepening AI innovation in conservation practices with contextual solutions tailored to China.