<p>Droplet dynamics is a critical area of study with significant implications across various fields, including industrial processes and biological systems. This paper presents a novel methodology—Machine Learning-Enhanced Computational Fluid Dynamics (ML-CFD)—to predict energy dissipation mechanisms in droplet dynamics and their effects on wetting phenomena. We analyze primary energy dissipation mechanisms—viscous, interfacial, and thermal—and discuss their roles in influencing dynamic wetting behaviors, contact angle hysteresis, and droplet stability on solid surfaces. By examining relevant equations and models, we elucidate how viscous, interfacial, and thermal dissipation mechanisms collectively influence wetting characteristics. The findings underscore the importance of understanding energy dissipation in optimizing applications across microfluidics, material science, and surface engineering, ultimately enhancing predictive capabilities and informing the design of advanced materials and systems.</p>