Aerosol hygroscopicity strongly governs particle size, mixing state, and radiative effects, yet remains poorly constrained for organic aerosols due to their chemical complexity and limited observations. Here, we present laboratory-measured size-segregated hygroscopic properties of 22 organic compounds, including carboxylic acids, amino acids, sugars, and alcohols, using a hygroscopic tandem differential mobility analyzer (HTDMA) combined with chemical characterization by Aerosol Mass Spectrometry (AMS). Our results extend previous studies by resolving hygroscopic behaviour across the submicrometer size range most relevant to atmospheric processes and by systematically linking organic hygroscopicity (κ_org) across functional groups, as measured by AMS, with physicochemical properties. Structurally similar compounds may exhibit markedly different hygroscopic behavior, underscoring the role of molecular interactions. Similar to carbon chains, increased functionalization generally enhances hygroscopicity and induces a pronounced size dependence. Functional-group-based classifications from the AMS provide a useful approximation for estimating κ_org, but may not capture this complexity. Leveraging these laboratory constraints, we use a simple but extensible machine-learning framework that integrates laboratory-derived κ_org with ambient aerosol observations. The application of this hybrid approach to urban and rural environments demonstrates substantial improvements in predicting ambient hygroscopicity, with R² values increasing from 0.82 to 0.96 at the Paris suburban site SIRTA (France) and from 0.60 to 0.94 at the rural background site Goldlauter (Germany), compared to conventional composition-based models. By bridging controlled laboratory measurements with data-driven ambient analysis, this study provides a rigorous pathway to improve the representation of the direct aerosol radiative effect in atmospheric and climate models.