Nanocarrier-based drug delivery systems have emerged as a promising strategy for improving therapeutic efficacy, minimizing adverse effects, and enabling controlled drug release. However, the development of optimized nanocarriers often requires extensive experimental trials involving diverse material doi:10.48047/AFJBS.7.12.2025.198-206 compositions, particle characteristics, and environmental conditions. Such procedures are time-consuming, costly, and may delay translational research efforts. Machine learning (ML) techniques provide an alternative approach by enabling predictive modeling of drug release behavior from experimental datasets. This study proposes a machine learning-based framework for predicting nanocarrier drug release profiles using physicochemical properties of nanoparticles and formulation parameters. A dataset comprising multiple nanocarrier formulations, including polymeric nanoparticles, liposomes, and lipid-based systems, was utilized for model development. Features such as particle size, zeta potential, encapsulation efficiency, drug loading capacity, polymer concentration, and pH conditions were considered as predictive variables. Several machine learning algorithms, including Linear Regression, Random Forest, Support Vector Regression, and Gradient Boosting Regression, were evaluated. Performance was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²). Experimental results demonstrated that ensemble-based methods achieved superior prediction accuracy compared with traditional regression techniques. The proposed framework enables rapid estimation of release kinetics and supports data-driven optimization of nanocarrier formulations. The findings highlight the potential of machine learning for accelerating intelligent drug delivery research and reducing dependence on extensive laboratory experimentation. This approach may contribute to the development of personalized and adaptive drug delivery systems in future pharmaceutical applications