Surface Pourbaix diagrams are critical to understanding the stability of nanomaterials in electrochemical environments. Their construction based on density functional theory (DFT) is, however, prohibitively expensive for real-scale systems, such as several nanometer-size nanoparticles (NPs) involving at least thousands of noble metal atoms, and this limitation calls for machine learning (ML)-driven approaches. Herein, with the aim of accelerating the accurate prediction of adsorption energies for a wide range of surface coverages on large-size NPs, we developed a bond-type embedded crystal graph convolutional neural network (BE-CGCNN) model in which four bonding types were treated differently. Owing to the much enhanced accuracy of the bond-type embedding approach compared to the original CGCNN, we demonstrate the construction of reliable Pourbaix diagrams for very large-size NPs involving up to 6,525 atoms (approximately 4.8 nm in diameter), which enables the exploration of electrochemical stability over various NP sizes and shapes. We reveal that ML-based Pourbaix diagrams well reproduce the experimental observations with increasing NP size, such as the increasing O- to OH-covered phase ratio and the decreasing Pt dissolution phase in the diagrams. This work suggests a new method for accelerated Pourbaix diagram construction for real-scale and arbitrarily shaped NPs, which would significantly open up an avenue for electrochemical stability studies.