Rahmati frames artificial intelligence as an ecological sensing and coordination apparatus. The iconic proposition is that urban biodiversity can be monitored through the integration of remote sensing, acoustic data, citizen science and predictive modelling, producing a multi-source account of species presence and habitat change. The theoretical contribution lies in treating cities as both biodiversity threats and conservation environments whose fragmented ecologies require adaptive, technologically augmented management. Methodologically, the proposed framework links species detection, invasive-species monitoring, ecosystem analysis and decision support, while stressing standardised data collection, model validation, equitable access and ethical safeguards. Its conceptual operation is computational amplification: dispersed ecological signals are converted into actionable patterns for conservation planning. The wider bridge joins urban ecology, AI governance, smart-city research and environmental ethics. The paper’s most important tension concerns legibility: enhanced detection may improve ecological care, yet monitoring systems also determine which species, habitats and communities become data-rich, potentially reproducing inequalities through uneven sensor coverage, technical capacity and institutional attention.