Asian Journal of Pure and Applied Mathematics

Biodiversity conservation is a pressing global challenge, as ecosystems face increasing threats from habitat loss, climate change, and human activity. Traditional conservation strategies often rely on limited field observations and heuristic approaches, which may not fully capture the dynamic interactions among species, environmental factors, and human interventions. Artificial intelligence (AI) offers advanced computational tools to analyse large-scale environmental data, uncover patterns, and optimise conservation strategies. When combined with Evolutionary Game Theory (EGT), AI provides a powerful framework to model species interactions, competition, cooperation, and resource allocation, enabling predictions of ecosystem dynamics under varying scenarios. This paper explores AI-driven biodiversity conservation strategies that integrate EGT with environmental datasets, including climate variables, land-use patterns, and species occurrence records. By leveraging machine learning models, such as reinforcement learning, neural networks, and optimisation algorithms, in combination with evolutionary game frameworks, conservation policies can be designed that promote sustainable species interactions, habitat preservation, and resilience against environmental changes. Case studies demonstrate how AI-EGT models can identify critical habitats, predict species population trajectories, and guide adaptive management practices. The results suggest that integrating AI with evolutionary game theory enhances decision-making capabilities, supports real-time monitoring, and facilitates proactive conservation measures. This approach also highlights the potential for multi-agent simulations, scenario analysis, and dynamic policy evaluation to improve biodiversity outcomes. The convergence of AI, EGT, and environmental data represents a promising frontier in ecological research, offering quantitative, data-driven solutions to complex conservation challenges.