In this study, 58 distinct TiO₂-coated glass samples were synthesized via Atmospheric Pressure Chemical Vapour Deposition (APCVD) under controlled synthesis conditions. The crystal properties, optical properties, surface properties and photogenerated charge carrier behaviour of all synthesized samples were characterized by X-ray diffraction (XRD), UV-visible spectroscopy, atomic force microscopy (AFM), and transient absorption spectroscopy (TAS), respectively. The photocatalytic activity of all coatings was systematically assessed against NO gas under near-ISO (22 197-1:2016) test conditions. The most active TiO₂ coating showed ∼22.3% and ∼6.6% photocatalytic NO and NO_x conversion efficiency, respectively, with this being ∼60 times higher than that of a commercial self-cleaning glass. In addition, we compared the accuracy of different machine learning strategies in predicting photocatalytic oxidation performance based on experimental data. The errors of the best strategy for predicting NO and NO_x removal efficiency on the entire data set were ±2.20% and ±0.92%, respectively. The optimal ML strategy revealed that the most influential factors affecting NO photocatalytic efficiency are the sample surface area and photogenerated charge carrier lifetime. We then successfully validated our ML predictions by synthesising a new, high-performance TiO₂-coated glass sample in accordance with our ML simulated data. This sample performed better than commercially available self-cleaning glass under a new metric, which comprehensively considered the visible light transmittance (VLT), NO degradation rate and NO₂ selectivity of the material. Not only did this research provide a panoramic view of the links between synthesis parameters, physical properties, and NO_x removal performance for TiO₂-coated glass, but also showed how ML strategies can guide the future design and production of more effective photocatalytic coatings.