Neural Green's Function is a novel neural solution operator specifically designed to address linear partial differential equations by leveraging principles inspired by traditional Green's functions. This approach emphasizes the role of domain geometry, which is central to its enhanced performance in solving such equations. Notably, Neural Green's Function demonstrates strong generalization capabilities, effectively handling a variety of irregular geometries as well as diverse source and boundary functions. These features collectively contribute to its robustness and adaptability across different problem settings. The method's foundation in classical Green's function theory allows it to maintain continuity with established mathematical frameworks while introducing neural network-based innovations. Recent connected research further contextualizes its focus on domain geometry and its theoretical underpinnings. Overall, Neural Green's Function represents a significant advancement in the application of neural operators to partial differential equations, combining traditional insights with modern machine learning techniques.