Meshless Particle Methods are a relatively new approach in the field of computational fluid dynamics (CFD), which has attracted significant attention over the last two decades [1]. Their popularity is largely due to their ability to circumvent the mesh tangling problem, which provides some unique advantages in modeling multi-physics flows and associated transport phenomena, albeit at the cost of computational power. In this class of methods, the grid is completely abandoned, and the discrete viscous flow is represented by replacing the conventional mesh with a finite number of particles that carry the fluid's characteristic properties, such as density, velocity, pressure, and other hydrodynamic properties in a Lagrangian manner. These particles essentially substitute the nodes in conventional mesh-based techniques. The fluid system's evolution is governed by interactions between these particles. Some examples are, but not limited to [2]: Smoothed particle hydrodynamics, Dissipative particle dynamics, Discrete Element Method, Reproducing kernel particle method, Moving particle semi-implicit, Particle-in-cell, Moving particle finite element method, Cracking particles method, Immersed particle method, Lattice Boltzmann Method, etc. On the other hand, as these techniques are still developing CFD methods, it is crucial to identify their advantages and limitations in modeling realistic multi-physics flow problems of real-life applications and of industrial interest [3]. Toward this end, this session aims at presenting motivations, current state, and challenges behind solving the relevant partial differential equations utilizing these methods, advancing their state-of-the-art application for addressing industrial problems, as well as benchmarking and deriving general conclusions regarding their benefits and limitations and stressing the remaining challenges to make them hand-on computational tools. Regarding the methodologies, we also look for novel developments and the extension of current numerical models/ algorithms that make the simulations of complex processes using such methods more reliable and/or faster. Some examples include novel methodologies for high-performance computing, neural network techniques, and Lagrangian tracking, among others. Additionally, step-by-step benchmarking as well as developing the capabilities of Meshless Particle Methods for new multi-phase, multi-scale, and multi-physics problems are of interest at the current symposium.