In recent years, ferrofluids have found increased popularity as a material for medical applications, such as ocular surgery, gastrointestinal surgery, and cancer treatment, among others. Ferrofluidic robots are multifunctional and scalable, exhibit fluid properties, and can be controlled remotely; thus, they are particularly advantageous for such medical tasks. Previously, ferrofluidic robot control has been achieved via the manipulation of hand-held permanent magnets or in current-controlled electromagnetic fields resulting in two-dimensional position and shape control and three-dimensional (3D) coupled position–shape or position-only control. Control of ferrofluidic liquid droplet robots poses a unique challenge where model-based control has been shown to be computationally limiting. Thus, in this study, a model-free control method is chosen, and it is shown that the task of learning optimal control parameters for ferrofluidic robot control can be performed using machine learning. Particularly, we explore the use of Bayesian optimization to find optimal controller parameters for 3D pose control of a ferrofluid droplet: its centroid position, stretch direction, and stretch radius. We demonstrate that the position, stretch direction, and stretch radius of a ferrofluid droplet can be independently controlled in 3D with high accuracy and precision, using a simple control approach. Finally, we use ferrofluidic robots to perform pick-and-place, a lab-on-a-chip pH test, and electrical switching, in 3D settings. The purpose of this research is to expand the potential of ferrofluidic robots by introducing full pose control in 3D and to showcase the potential of this technology in the areas of microassembly, lab-on-a-chip, and electronics. The approach presented in this research can be used as a stepping-off point to incorporate ferrofluidic robots toward future research in these areas.