Poor aqueous solubility persists as a significant challenge in the pharmaceutical industry.Ongoing research aims to enhance the solubility of drugs to deliver them more effectively. Amorphous solid dispersion (ASD) is a widely used solubility enhancement technique. The absence of a specific model to predict compound solubility from ASDs resulted in a trial-and- error approach to studying solubility enhancement and makes it a laborious and time-consuming process. Predictive models could streamline this process and accelerate the development of oral drugs with improved aqueous solubilities. This study aimed to develop a predictive model to estimate the solubility of a compound from the polymer matrices in ASDs. For this purpose, five BCS Class IV drugs (acetazolamide, chlorothiazide, furosemide, hydrochlorothiazide, sulfamethoxazole), four hydrophilic polymers (PVP, PVPVA, HPMC E5, Soluplus), and a surfactant (TPGS) were chosen as the models for drug, polymers, and surfactant, respectively. ASDs of model drugs were prepared using hotmelt process. The prepared ASDs were characterized using DSC, FTIR, and XRD. The aqueous solubility of the model drugs was determined using the shake-flask method. Multiple linear regression was used to develop a predictive model to determine aqueous solubility using the molecular descriptors of the drug and polymer as predictor variables. The model was validated using Leave-One-Out Cross-Validation. The ASDs’ drug components were identified as amorphous via DSC and XRD Studies.There were no significant chemical interactions between the model drugs and the polymers based on FTIR studies. Compared with pure drugs, their ASDs showed a significant (p