Hot-arid cities such as Dubai experience strong urban heat island effects driven by dense urban morphology, heat-absorbing surfaces, and anthropogenic heat. Passive cool materials—engineered for high solar reflectance and high mid-IR emissivity—offer a scalable pathway to lower surface and near-surface air temperatures, reduce cooling loads, and cut peak electricity demand. Current solutions span high-albedo paints (TiO₂/BaSO₄), retroreflective finishes, and radiative-cooling ceramics that scatter sunlight while emitting strongly in the 8–13 µm atmospheric window. Building-energy studies report substantial reductions in cooling energy, while hierarchically porous ceramics (e.g., alumina) have reached near-unity solar reflectance and high emissivity, with promising roof-scale demonstrations. Scalable manufacturing routes—including thermal spray and airless spray—bridge lab materials to large-area deployment for high performance durable coatings. In parallel, machine learning has progressed from physical and mechanical properties prediction to surrogate modeling and inverse design, and digital twins with robotic deposition are emerging to make application repeatable and adaptive. The main challenges remain: long-term durability and reproducible ceramic porosity/roughness on complex geometries. Our team has developed physics-based multiphase and heat-transfer models for particle impact and solidification. In addition, we built GNN surrogates to accelerate simulations of the thermal-spray process and generative artificial neural networks (GAN) pipelines to map spray conditions to coating microstructure, paving the way—within this project—for inverse design of spray “recipes” that target high reflectance and emissivity under durability constraints. Recently, we also developed a model for conjugate heat transfer in urban flows, which could help with actionable planning metrics for buildings coverage and material selection.