In pharmacological research and development, applying machine learning (ML) in drug delivery simulations marks a radical change ( ). Often, depending on empirical data and crude computational models, conventional drug delivery systems can be time-consuming and less exact. Still, machine learning offers a vital substitute by using large databases to predict drug behaviour, streamline delivery systems, and raise treatment outcome accuracy ( ). Using cutting-edge algorithms and data-driven insights, MLdriven drug delivery simulations faithfully replicate complex biological systems, identify the most efficient delivery paths, and project tailored patient reactions. This approach not only accelerates the development of new pharmaceuticals but also provides opportunities for tailored medicine, ensuring that treatments are adjusted to fit every patient's particular requirements and produce the best possible effectiveness and the fewest possible side effects. The advent of machine learning (ML) in medication delivery models has the power to revolutionise the whole industry. Increasing speed, efficiency, and accuracy above past capabilities ( ) can improve drug development (Figure ). The pharmaceutical industry is increasingly adopting AI techniques, including machine learning, to enhance the effectiveness of drugs in terms of their bioavailability, stability, and ability to target specific organs. Computational pharmaceutics transforms medication delivery paradigms by utilising big data and multiscale modelling techniques. Machine learning allows for identifying relevant characteristics, the discovery of unexpected patterns, and immediately adjusting to pathogen evolution, hence allowing effective treatment development, selection, and dosage optimisation. Machine learning integrated into clinical devices can significantly transform clinical decision-making in anti-infective drug delivery strategies, thanks to its impressive speed and practicality ( ).