Abstract: The unique properties exhibited in immiscible metals, such as excellent strength, hardness, and radiation-damage tolerance, have stimulated the interest of many researchers. As a typical immiscible metal system, the Cu-W nano-multilayers combine the plasticity of copper and the strength of tungsten, making it a suitable candidate for applications in aerospace, nuclear fusion engineering, and electronic packaging, etc. To understand the atomistic origin of the defects (e.g., vacancies, free surfaces, grain boundaries, and stacking faults and thermodynamical properties), we developed an accurate machine learning interatomic potential for Cu-W based on the atomic cluster expansion (ACE) method. The Cu-W ACE potential can faithfully reproduce the fundamental properties of Cu and W predicted by density functional theory (DFT) calculations. Moreover, the thermodynamical properties, such as the melting point, coefficient of thermal expansion, diffusion coefficient, and equation of the state curve of the Cu-W solid solution, are calculated and compared against DFT and experiments. Monte Carlo molecular dynamics simulations performed with the Cu-W ACE potential predict the experimentally observed phase separation and uphill diffusion phenomena. Our findings not only provide an accurate ACE potential for describing the Cu-W immiscible system but also shed light on understanding the atomistic mechanism during the Cu-W nano-multilayers formation process.