Fusion energy has the potential to change the global energy economy and provide an essential technology to stop climate change[1]. The most promising candidates to realize a fusion power plant are tokamaks and stellarators confining the burning plasma with magnetic fields. Tokamaks are rotationally symmetric and use a large plasma current to achieve confinement. In contrast, Stellarators are non-axisymmetric and employ three-dimensionally non-planar magnetic field coils to twist the field and confine the plasma. Advantages such as disruption-free and steady-state operation [2], [3] make stellarators very attractive for such goals. Moreover, it reduces control systems issues and mitigates mechanical problems due to load cycling [4]. One of the goals of stellarator optimization is to find the best set of coils to produce the desired magnetic field. Exploring this design space requires new methods that can reduce the costly computations associated with modeling stellarator designs. This work uses Bayesian optimization, (BO) [5] which is a method that is well suited to black-box optimizations. It leverages surrogate models that are constructed to integrate as much information as possible from the available data points. This allows an optimization algorithm that significantly reduces the number of model evaluations as compared to alternative methods. This study showcases the efficacy of Bayesian optimization as a versatile tool for enhancing both magneto-static and mechanical properties within stellarator winding packs. Employing a suite of Bayesian optimization algorithms, encompassing constrained and multi-objective optimization techniques [6], [7], [8] we iteratively refine 2D and 3D models of solenoid and stellarator configurations. As fusion technology progresses from experimental stages to commercial viability, the demand for precise and efficient design methodologies escalates. By emphasizing its modularity and transferability, our approach lays the foundation for streamlining optimization processes, facilitating the integration of fusion power into a sustainable energy infrastructure.