Critical evaluation of the current state of nanomedicines in both the clinic and in research is vital to realizing its full potential. To augment the process of designing and developing new nanomedicines, it is necessary to employ tools and techniques that address issues arising from the traditional trial-and-error approach to pharmaceutical formulation development. Indeed, the current approach begets a lack of standardization and consequently challenges in reproducibility, as well as a less efficient path towards achieving clinically relevant formulations. As we have mentioned previously, automation and ML can help tackle these issues; however, a natural question becomes, what might an integrated workflow with these tools look like? As a simple case, we consider how automation and ML complement each other to advance the steps in conceiving and realizing new formulations. Initially, a pharmaceutical scientist is tasked with formulating an API to address a specific challenge, such as poor solubility or instability of the API in the bloodstream. Certain materials and target physicochemical properties may also be defined for the nanoformulation given the problem to be addressed. Knowing that the eventual goal is data-driven design of the nanomedicine, the scientist recognizes that they need an appropriate dataset. This can be sourced from the literature, which is complicated given the potential scarcity of material and variety of reporting standards. Alternatively, the data can be obtained experimentally. Performing either of these tasks by hand is onerous, so after careful consideration, the scientist decides to employ a robotic platform to prepare their proposed formulation and generate in-house data at a faster pace and on a more reproducible scale. The resultant dataset serves as a starting point for modeling as well as the curation of publicly available data, which forms the basis of future formulations using a similar drug delivery system. Depending on the initial goal and predetermined qualities, the ML models can effectively predict performance and/or characteristics of the formulation. The formulation may then be tested experimentally to verify relevant formulation parameters and fed back into models or studied further in vitro. The workflow is cyclical in nature, with the key steps being the use of experimental automation to obtain the foundational data for ML methods, which in turn, direct the development of the drug formulation.