Nanocarrier-based drug delivery systems have attracted considerable attention because of their ability to improve drug stability, enhance bioavailability, and enable controlled therapeutic release. Accurate prediction of drug release profiles is essential for optimizing nanocarrier formulations and reducing experimental development time. Conventional approaches often rely on laboratory-based release studies, which can be resource-intensive and time-consuming. Machine learning techniques provide an alternative approach for modeling complex relationships between formulation parameters and drug release behavior. This paper presents a machine learning-based framework for predicting nanocarrier drug release profiles using formulation characteristics and physicochemical properties. The proposed framework integrates data preprocessing, feature engineering, predictive modeling, and explainability analysis to estimate cumulative drug release behavior. Key formulation variables, including particle size, polymer concentration, drug loading efficiency, zeta potential, encapsulation efficiency, and release medium characteristics, are utilized as predictive inputs. A Random Forest regression model is employed to estimate drug release percentages at different time intervals, while Shapley Additive Explanations (SHAP) are incorporated to identify influential formulation factors. Experimental evaluation is conducted using nanocarrier drug release datasets obtained from published pharmaceutical studies. Model performance is assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²). Results indicate that the proposed framework accurately predicts release profiles and provides interpretable insights regarding formulation variables influencing drug release kinetics. The findings suggest that machine learning-based prediction models can support formulation optimization and facilitate data-driven development of intelligent drug delivery systems.