There has been a lot of discussion about accelerating the development of materials in order to improve properties for advanced applications, reduce the expensive time to market cycle, and engineer new functional properties. One of the most significant challenges for machine learning models in materials development is the lack of available experimental data. In this presentation, an accelerated ceramics processing laboratory using dry starting powders will be presented, where issues related to human interaction time, parallelization versus automation, and specific challenges for each step will be introduced. As a model system, the high entropy perovskite oxide system (Bi0.2Na0.2Ba0.2Sr0.2Ca0.2)TiO3 (BNBSCT) will be discussed. A-site disordered lead-free ferroelectrics have attracted considerable interest due to their enhanced functional properties and increased temperature stability for energy storage applications, although there is a significant lack of information regarding the role of disorder as well as in the synthesis that limits cross-study reproducibility. Here, novel accelerated synthesis and characterization methods based on established and highly transferable methods allow for rapid investigation of this ceramic system. In this study, the influence of different precursor materials, including carbonates, oxides, and compounds, as well as the calcination and sintering temperatures on macroscopic dielectric and energy storage properties as well as crystal structure of BNBSCT was investigated. Using these methods, 240 different samples were produced and characterized, providing and unprecedented insight into this material system. Finally, opportunities and possible future research directions in the development of ferroelectrics and other functional ceramics using accelerated synthesis and processing together with ML methods will be discussed.