A Review of AI Techniques for Predicting and Optimizing Biological Magnification in Terrestrial Ecosystem

Biological magnification is the process through which pollutants get amplified as they move up the food chain. This poses a significant environmental and health challenge. Traditional approaches to monitoring and controlling pollutants such as physical, chemical and biological methods are often limited in scope and efficiency. However, recent breakthroughs in artificial intelligence present transformative potential to address the complexities of biological magnification. This review explores AI technologies such as predictive modelling, machine learning, and data-driven analytics for monitoring, assessing, and mitigating the impacts of pollutant accumulation. Moreover, AIbased systems enhance real-time monitoring using sensors and drones achieving 90% accuracy and a precision rate exceeding 85% in predictive modelling to identify high-risk areas. These systems also optimize remediation strategies through data analysis. Deep learning and computer vision tools enhance the identification of pollutant sources and their ecological impact. Additionally, AI-based risk assessment frameworks and decision support systems enable cost-effective and sustainable solutions. This review emphasizes the need for interdisciplinary research in advancing AI applications for biological magnification, addressing critical gaps in existing methodologies, and providing actionable insights for policymakers and environmental managers. The findings indicate a necessity of integrating AI with environmental monitoring frameworks to produce even more precise, efficient, and scalable solutions to biological magnification combat, in favor of safe ecosystems and human health.