Advancements in nanomedicine present potential directions and openings for targeted drug delivery and diagnostics with therapeutic intervention. Nanomedicine has the potential application in almost every aspect of the health sectors including targeted drug delivery, moderation of physicochemical properties of active pharmaceutical ingredients, diagnosis, and many more. Almost every disease, including cancer and neurological and cardiac disorders, is being treated with nanomedicine. Nanomedicine has also shown its potential role in several emerging therapies such as gene therapy, immunotherapy, phototherapy, and others. However, on the practical ground, the prediction of the actual performance of nanomedicine within the biological media is a little difficult due to complex bio-nano interactions. As predictive models through computations became available, this allows simulations that can analyze nanomedicine properties at a molecular scale. This chapter discusses the development and application of computational models, including molecular dynamics, quantitative structure-activity relationship models, and machine learning algorithms, for predicting the physicochemical characteristics, biodistribution, and therapeutic outcomes of nanomedicines. The integration of such models with experimental data helps researchers streamline the design and optimization process and reduces the amount of extensive in vitro and in vivo testing needed. It aims to cover significant progress that has been achieved in using computational tools to improve the accuracy and efficiency in nanomedicine development towards personalized and effective healthcare solutions.