Controlled-release matrix tablets require careful balancing of polymer characteristics, drug solubility, and compression conditions to achieve a desired dissolution profile. Because these factors influence drug release through swelling, diffusion, erosion, and changes in tablet porosity, formulation development often relies on repeated experimental trials and design-of-experiments approaches. This can be time-consuming when several material and process variables interact within a large formulation space. This manuscript presents a predictive modelling approach for estimating the complete dissolution profile of controlled-release tablets using key formulation and manufacturing inputs. The proposed framework uses polymer molecular weight and viscosity, drug solubility in aqueous and buffered media, compression pressure, dwell time, and tablet porosity as model features. A gradient-boosted multi-output regression model is conceptually applied to predict percent drug released at multiple time points, with the predicted profile optionally linked to a kinetic model such as the Weibull function. Rather than estimating only a single dissolution endpoint, the model is designed to forecast the entire release curve and provide insight into the variables that influence early, intermediate, and late release phases. Such a framework could allow polymer grade selection, solubility adjustment, and compression settings to be evaluated before tablet manufacture. Overall, this modelling strategy may reduce experimental burden, improve formulation efficiency, and support Quality-by-Design and real-time release testing. Its successful application would require transparent feature selection, mechanistically consistent curve prediction, and prospective experimental validation.