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@article{207867,
        author = {Dr PULIPATI SOWJANYA and Y Dinesh and K Vandana and K Sri Ram},
        title = {The Role of Machine Learning in Enhancing Nanoparticulate Drug Delivery Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {3075-3092},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207867},
        abstract = {Nanoparticulate Drug Delivery Systems (NDDS) have emerged as promising approaches for improving therapeutic efficacy through targeted drug delivery, enhanced solubility and bioavailability, controlled drug release, and reduced systemic toxicity. However, the design and optimization of nanoparticulate systems remain challenging because of the complex interactions among particle size, morphology, surface charge, drug loading, formulation composition, biological barriers, biodistribution, and toxicity. Conventional trial-and-error approaches are often time-consuming and inefficient when dealing with such multidimensional formulation variables. Machine Learning (ML), a key component of artificial intelligence (AI), offers a data-driven strategy to address these limitations by identifying complex relationships within formulation and biological datasets and predicting optimal nanoparticle characteristics. This review highlights the role of ML in the design and optimization of NDDS, including prediction of nanoparticle size, shape, surface properties, encapsulation efficiency, drug loading, release kinetics, stability, pharmacokinetics, biodistribution, and cellular uptake. Different ML approaches, including supervised learning, unsupervised learning, reinforcement learning, neural networks, ensemble methods, and deep learning, are discussed in relation to their applications in nanoparticle development and personalized drug delivery. ML-assisted approaches can reduce experimental iterations, improve formulation optimization, enhance targeting specificity, and facilitate the development of adaptive and stimuli-responsive nanocarriers. Despite these advances, challenges related to data quality and availability, standardization, model interpretability, algorithmic bias, regulatory validation, and clinical translation remain. Future integration of ML with multimodal datasets, physiologically based pharmacokinetic models, Internet of Things (IoT)-enabled healthcare, explainable AI, and in silico trials may further accelerate the development of safer, more effective, and personalized nanomedicines. Overall, the integration of ML with NDDS represents a significant step toward predictive, intelligent, and patient-centric drug delivery systems.},
        keywords = {},
        month = {August},
        }