Environmental and Financial Asset Optimization through Multi-Restoration Informed by AI Systems for Optimizing Ecosystem Functioning

Environmental settings study the link between economic growth and resource exploitation because of the need for fair distribution. Economic resources pose difficulties due to the loss of ecosystems, soil deterioration, and long-term toxicity. For enhancing biodiversity, soil deterioration, and toxicity over the long term in environmental environments, the Artificial Intelligence-based Multi-Restoration Ecological Optimization Framework (AI-MEOF) is presented in this research. Reduced reliance on ozone-depleting products, conservative electricity usage, and lower service prices are the results of using resource surface prediction analysis. By lowering the chance of being exposed to possibly dangerous chemicals in the environment, AI-MEOF incorporates probabilistic diversity analysis. The study provides an empirical foundation for decision-making to preserve resource-dependent economies' long-term health, stability, and reasonableness. Analysis of the suggested framework through simulation demonstrates its dependability concerning sensitiveness, achievement, and efficiency.