<p>The ongoing decline in both intra- and interspecific plant diversity poses a critical threat to global biodiversity, particularly for endangered species. The integration of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) algorithms, offers innovative solutions for enhancing conservation through rapid and data-driven decision-making. Recent advancements in data science have facilitated the application of AI in modernizing and optimizing conservation strategies. This review conducts a comprehensive bibliometric analysis of literature indexed in Scopus from 1993 to 2024, examining trends in publication output, citation patterns, international collaborations, keyword co-occurrence, and thematic focus areas. The findings highlight a marked increase in AI-related research in endangered plant species conservation, with significant contributions from countries such as the USA. AI-based tools support various applications, including genomic selection, high-throughput phenotyping, and optimization of in-vitro propagation techniques, all critical for ex-situ and in-situ conservation. These technologies also enhance real-time monitoring, predictive modelling, and biodiversity management. However, the adoption of AI remains limited due to challenges such as data scarcity, model generalizability, high computational costs, and limited infrastructure in biodiversity-rich but resource-constrained regions. This review provides a systematic overview of AI-driven models and discusses their potential for integration into practical conservation frameworks. Emphasis is placed on the development of interpretable, scalable, and resource-efficient AI models, alongside the need for inclusive approaches that incorporate local knowledge and ethical considerations. The insights aim to guide future research and implementation strategies for improving the conservation of endangered plant species using AI technologies.</p>