Abstract Producing optimized and accurate transmission spectra of exoplanets from telescope data has traditionally been a manual and labor intensive procedure. Here we present the results of the first attempt to improve and standardize this procedure by using artificial-intelligence-based (AI-based) processing of light curves and spectroscopic data from transiting exoplanets observed with the Hubble Space Telescope's (HST) Wide Field Camera (WFC3) instrument. We implement an AI-based parameter optimizer that autonomously operates the Eureka! pipeline to produce homogeneous transmission spectra of publicly available HST WFC3 datasets, spanning exoplanet types from hot Jupiters to sub-Neptunes. Surveying 42 exoplanets with temperatures between 280 and 2580 K, we confirm modeled relationships between the amplitude of the water band at 1.4 μm of hot Jupiters and their equilibrium temperatures. We also identify a similar, novel trend in Neptune/sub-Neptune atmospheres, but shifted to cooler temperatures. Excitingly, a planet-mass versus equilibrium-temperature diagram reveals a “Clear Sky Corridor,” where planets between 700 and 1700 K (depending on the mass) show stronger 1.4 μm H2O band measurements. This novel trend points to metallicity as a potentially important driver of aerosol formation. With HST sculpting this foundational understanding for aerosol formation in various exoplanet types ranging from Jupiters to sub-Neptunes, we present a compelling platform for the James Webb Space Telescope to discover similar atmospheric trends for more planets across a broader wavelength range.