Amid escalating global environmental challenges, the banking sector is increasingly turning to Artificial Intelligence (AI) to enhance Environmental Sustainability Performance (ESP). Our research examines the impact of AI adoption on ESP through the lenses of sustainable banking, Fintech, green finance, and green innovation within China's banking institutions. We also explore the complex configurations of these factors that collectively improve ESP. Grounded in the Stimulus-Organism-Response (SOR) and Affordance theories, we employ a hybrid methodology combining Structural Amidst escalating global environmental challenges, the banking sector is increasingly turning to artificial intelligence (AI) to enhance environmental sustainability performance (ESP). Our research examines the impact of AI adoption on ESP through the lenses of sustainable banking, Fintech, green finance and green innovation within China's banking institutions. We also explore the complex configurations of these factors, which collectively improve ESP. Grounded in the stimulus–organism–response and affordance theories, we employ a hybrid methodology combining structural equation modelling and fuzzy-set qualitative comparative analysis to analyse data from an online survey. Our findings indicate that AI adoption significantly boosts ESP in the banking sector, primarily mediated by sustainable banking and green innovation, despite Fintech showing no significant direct impact on ESP. Additionally, we identify specific configurations of AI, sustainable banking, Fintech, green finance and innovation that synergistically enhance ESP, contributing to the ongoing discourse on technological innovation and sustainability in the banking industry. This study emphasizes the pivotal role of AI in driving sustainable outcomes and highlights the need for strategic integration of these factors to achieve higher ESP.