The redox potential of protein-bound metallocofactors is a key determinant of their function, with broad implications across fundamental research and applied fields, including bioengineering, biosensing, biocatalysis, energy conversion, and life sciences.1 Beyond its determination, a major challenge lies in understanding how the protein matrix modulates redox properties, particularly in metallocofactors, where multiple structural and electronic factors interplay. Experimental methods for measuring protein redox potentials are labor-intensive, while standard computational approaches are highly demanding, making them impractical for large-scale studies. However, high-throughput predictive models are essential to guide the rational design of metalloproteins with tailored redox properties.2 Here, we present a data-driven approach for predicting the redox potential of metalloproteins, using iron-sulfur (FeS) proteins as a case study. We developed a machine learning (ML)-based model trained on a curated dataset of experimentally characterized FeS proteins. Ad hoc descriptors, capturing the physicochemical properties of the protein environment, are automatically extracted from protein structures, incorporating short-, medium-, and long-range effects. As an initial step, we focused on simple FeS cofactors, including Fe₂S₂ ferredoxins, Rieske-type proteins, mitoNEET, and rubredoxins, which are well-characterized and biologically relevant.3 Our results demonstrate that this ML-based strategy enables highly accurate redox potential predictions, outperforming traditional computational methods. Specifically, this approach achieved an error below 1 kcal/mol (~37 mV), comparing favorably with QM-based approaches while being computationally more efficient.4 Moreover, this approach identifies key protein features that quantitatively modulate redox potential. Preliminary results also indicate that the model successfully predicts the redox potential of Fe₂S₂ clusters in more complex protein environments, laying the groundwork for a generalizable strategy applicable to larger FeS clusters and other metalloproteins. This paves the way for efficient computational screening tools in protein engineering and redox biology.