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Umami peptides have recently gained attention for their ability to enhance umami flavor, reduce salt content, and provide nutritional benefits. However, traditional wet laboratory methods to identify them are time-consuming, laborious, and costly. Therefore, we developed the Umami-gcForest model using the deep forest algorithm. It constructs amino acid feature matrices using ProtBERT, amino acid composition, composition-transition-distribution, and pseudo amino acid composition, applying mutual information for feature selection to optimize dimensions. Compared to other machine learning baseline, umami peptide prediction, and composite models, the validation results of Umami-gcForest on different test sets demonstrated outstanding predictive accuracy. Using SHapley Additive exPlanations to calculate feature contributions, we found that the key features of Umami-gcForest were hydrophobicity, charge, and polarity. Based on this, an online platform was developed to facilitate its user application. In conclusion, Umami-gcForest serves as a powerful tool, providing a solid foundation for the efficient and accurate screening of umami peptides., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)