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Abstract The restoration of degraded ecosystems is critical for mitigating climate change and reversing biodiversity loss. Depending on the primary objective – such as maximizing carbon sequestration or protecting threatened species – and within the boundaries of budget constraints, different spatial priorities have been identified at global and regional scales. Funding mechanisms to support such work comprise public sources, philanthropy, and the private sector, including the sales of carbon and biodiversity credits. However, effectively exploring tradeoffs among restoration objectives and estimating the price of biodiversity and carbon credits to design financially viable projects remain challenging. Here we harness the power of artificial intelligence in our software CAPTAIN, which we further develop to identify spatial priorities for ecological restoration that maximize multiple objectives at once and to allow a robust evaluation of biodiversity and climate outcomes. We find through a series of simulations that even low-to-moderate consideration of biodiversity in restoration projects leads to the selection of restored areas that disproportionately improve the conservation of threatened species, while resulting in a relatively smaller total amount of carbon captured. We propose a data-driven valuation of biodiversity credits in relation to carbon credits, enabling the design of a bundled financial model that could support restoration efforts even in areas previously excluded for economic reasons. Applying our methodology to plant diversity in the Atlantic Forest of eastern South America, one of the most biodiverse and threatened ecosystems globally, we demonstrate its practical utility in guiding real-world restoration and quantifying the essential trade-offs between climate and nature outcomes. Our study provides a robust, scalable methodological pathway to optimize the outcomes of restoration efforts for climate and nature.