<p>Liposomal drug delivery systems have improved cancer therapeutics by enhancing drug stability, allowing selective tissue targeting, and reducing off-target effects. One of the main problems, however, is how to maximize drug-lipid interaction as well as develop personalized treatment alternatives. Traditional methods in computational biology, such as molecular dynamics simulations, are useful but have challenges in their scalability and cost of computation. This study focuses on the use of deep learning algorithms, Graph Neural Networks (GNNs), Attention Mechanisms, and Physics-Informed Neural Networks (PINNs) for the prediction and optimization of drug-lipid interactions in liposomal formulations. These models are much more advanced, can handle complex datasets with simplified models, and recognize complicated interaction patterns while adhering to the necessary physics involved in the problem. We highlight the practicality of these models in predicting encapsulation efficiency, drug release kinetics, and developing controlled drug delivery systems for cancer treatment through several case studies. Also, the application of transfer learning and meta-learning improves model transferability in different drug-lipid matrices, which is a step towards personalized medicine. Our results highlight that the combination of deep learning with experimental and clinical evidence enhances predictive performance and expands scope, thereby facilitating the formulation of more exact and individualized treatment modalities. Such an interdisciplinary approach can greatly improve treatment efficacy and expand the horizons of precision medicine in the field of nanomedicine.</p> Graphical Abstract <p>Advancing Liposomal Drug Delivery through Graph Neural Networks (GNNs) and Physics-Informed Neural Networks (PINNs).</p> <p></p>