Abstract: Plasma spraying technology plays a crucial role in coating preparation owing to its high efficiency and versatility. This technology heats and accelerates the spraying powder of metals, ceramics, polymers, and their composites through ultra-high temperature and high-speed jets and then sprays them onto the substrate surface to form high-performance coatings. However, the quality of the coating is influenced by various factors, such as particle flight behavior, process parameters, and substrate conditions, and controlling its microstructure remains challenging. This paper aims to systematically elucidate the correlation mechanisms between particle flight dynamics, spreading kinetics, and coating performance, providing a theoretical basis for achieving precise control of the coating microstructure. Research shows that particles undergo critical stages such as acceleration, heating, diffusion, and solidification during flight, and their behavior directly affects the final performance of the coating. Through particle diagnostic techniques and multi-physics field numerical simulations, the influence of process parameters, such as the Ar/H₂ flow rate, spraying power, and spraying distance, on particle temperature, velocity, and spreading morphology can be quantitatively analyzed. Furthermore, the regulatory effects of particle flight characteristics (such as Reynolds number and Weber number), post-collision diffusion behavior, and interface heat transfer mechanisms on the coating microstructure are revealed. The results indicate that precisely controlling the coupling of particle flight temperature and velocity is key to improving coating quality, and reasonable process parameters can effectively reduce defects such as pores and microcracks. In recent years, data-driven methods such as machine learning and deep transfer learning have shown potential in optimizing the spraying process. However, their practical industrial applications still have limitations. Current research mainly focuses on the particle behavior mechanism under ideal experimental conditions, while the influence of dust, vibration, and other interference factors in actual industrial environments has not been deeply explored. Moreover, online monitoring equipment incurs high costs. The operation is also complex, and the environmental adaptability is poor, making large-scale adaptation in industrial sites difficult. The interaction between different material systems (such as high-melting-point and low-electrical-conductivity ceramic materials) and process parameters also requires systematic research. To achieve the leap from laboratory to industrial sites for plasma spraying technology, it is necessary to continuously advance research in multiple directions: conduct more experiments and simulations close to actual working conditions to obtain reliable data and provide support for process optimization; develop real-time monitoring systems that adapt to harsh industrial environments to achieve dynamic integration of monitoring data and equipment control; systematically establish quantitative relationships between process parameters, material properties, and coating performance to guide targeted parameter design; and, finally, expand the application of machine learning in process autonomous optimization and intelligent coating design, promoting interdisciplinary cooperation and technological innovation. In summary, the development of plasma spraying technology requires the integration of knowledge from multiple disciplines, such as mechanical engineering, materials science, fluid mechanics, and data science. Through the dual promotion of basic research and engineering applications, coating performance can be improved, and the application scope of coatings can be expanded.