This paper explores the development of an AI-based model designed to create adaptive investment policies that balance environmental justice and economic growth. The model simulates economic systems of resource extraction and production, considering their environmental impacts on affected populations, especially vulnerable communities disproportionately harmed by pollution. In addition to addressing specific local impacts, the model accounts for global environmental issues, such as greenhouse gas emissions. Machine learning algorithms are used to optimize a payment structure known as JADE (Justice from Adaptive Dividends for the Environment), which requires polluters to invest in supporting the communities they affect. The model determines the optimal level of JADE payments to maximize both economic development and environmental justice. These payments mitigate local environmental damage while contributing to global climate goals. The study's results suggest that targeted investments by polluters, through an adaptive framework, can foster long-term economic growth and environmental sustainability. This work highlights the potential of AI-driven policy models to address critical challenges at the intersection of environmental justice and economic development.