Due to the global warming, a significant reduction in the emission of greenhouse gases is necessary. One part of the solution is the electrification of today's transportation and traffic sector. An essential component of today's electric vehicles is the lithium-ion battery (LIB), which is largely responsible for their range, performance and cost. In order to increase the use of such climate-friendly technologies, it is therefore essential to reduce the production costs of LIBs. With a duration of up to three weeks, the process steps of formation and aging are particularly capital-intensive and have high demands on storage capacities. Formation and aging therefore account for up to 30% of the manufacturing costs for battery cells. During formation, the solid electrolyte interphase (SEI) is formed, which has a major influence on the quality and lifetime of the LIB, among other things. In order to reduce production costs and simultaneously increase battery cell quality, it is therefore necessary to optimize the formation and aging process. Because of the complexity and the interdependency of these processes towards previous process parameters the application of machine learning algorithm is predestined to optimize these process steps. For this purpose, a general approach for the application of a machine learning algorithm in the formation and aging are first analysed and relevant parameters from the literature as well as reasonable assumptions about the structure are derived. Based on these requirements and boundary conditions a machine learning algorithm structure will be developed to optimize the cell finishing process in the battery cell production.