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Grain boundaries (GBs) can critically influence the microstructural evolution and various materials properties. However, a fundamental understanding of GBs in high-entropy alloys (HEAs) is lacking because of the complex couplings of the segregations of multiple elements and interfacial disordering, which can generate new phenomena and challenge the classical theories. Here, by combining large-scale atomistic simulations and machine learning, we demonstrate the feasibility of predicting the GB properties as functions of four independent compositional degrees of freedoms and temperature in a 5D space. Subsequently, GB counterparts to bulk phase diagrams are constructed for the first time for quinary HEAs. A data-driven discovery further reveals new coupled segregation and disordering effects in HEAs. Notably, an analysis of a large dataset discovers a critical compensation temperature at which the segregations of all elements virtually vanish simultaneously. While the machine learning model can predict GB properties via a black-box approach, a surrogate data-based analytical model (DBAM) is constructed to provide more physics insights and better transferability, with good accuracies. This study not only provides a new paradigm enabling prediction of GB properties in a 5D space, but also uncovers new GB segregation phenomena in HEAs beyond the classical GB segregation models.