Particle-based methods, like Smoothed Particle Hydrodynamics (SPH) and the Discrete Element Method (DEM), provide an intrinsically advantageous framework for physics-informed machine learning (PIML). Their meshfree, Lagrangian nature creates a natural synergy with neural networks, characterized by four key benefits: (1) Physics-driven dimensionality reduction, where neighbour lists act as physics-optimized, untrainable convolutional filters; (2) Strong physical consistency, achieved by learning only inter-particle forces while embedding Newtonian dynamics directly into the solver, thereby preserving conservation laws; (3) Scalable pairwise architectures that replace complex architectures with simple feedforward ANNs, improving interpretability and efficiency; and (4) Exceptional generalization across geometries and boundary conditions, demonstrated by models trained only on free-surface flow accurately predicting other types of flow. Validated in multiple inverse problems involving conservative, dissipative, and bounded particulate systems, this minimalist approach yields noise-tolerant, high-fidelity emulators. The results confirm that particle methods are not merely compatible with PIML, but constitute an optimal computational platform for interpretable, physics-consistent data-driven modelling of multi-phase, multi-physics systems.