This paper explores the intersection of General Equilibrium with Incomplete Markets (GEI) theory and Artificial Intelligence (AI), with a focus on the increasing importance of GEI in sustainable finance. GEI models are instrumental in analyzing equilibrium in economic settings with incomplete markets, capturing nuanced interactions under real-world constraints. As sustainable finance emphasizes robust and resilient economic frameworks, the need for adaptable GEI models becomes essential. Leveraging projection mappings in machine learning models, we introduce techniques such as neural networks for dimensionality reduction and parameter estimation to enhance GEI models’ computational efficiency and responsiveness to dynamic market conditions. This integration not only refines equilibrium analysis but also opens new avenues for understanding economic dynamics in complex, data-driven environments critical to sustainable finance.