Perovskite solar cells are among the most dominant emerging PV technologies which have shown unparallel improvement in power conversion efficiency in just over a decade. Long charge carrier diffusion lengths and lifetimes, high absorption coefficients, band gap tunability, and defect tolerance characteristics make them remarkable optoelectronic materials. However, the process of fabricating perovskite solar cells is very extensive and complex creating hindrance to the scaling-up of the solar cells. Plethora of choices for materials, processes, cell configurations, and storage environment or stabilizing period needs to be evaluated for different layers and steps involved in the complete fabrication of these cells. This study addresses the evaluation of decision variables for the complete fabrication of these cells with different objectives that need to be accounted at the experimentation stage along with detailed insights into different process steps. This evaluation of the entire process will be necessary for going forward for multi-objective optimization of the perovskite solar cell production which not only provides good efficiencies but also involves low energy-intensive fabrication processes like solution-based techniques, low-cost materials, and low-environmental impact. Along with this goal of critically evaluating the process design for perovskite solar cells, the study also suggests mechanistic models at different steps which can be employed to enhance the screening process for fabricating these cells. Finally, a framework for employing mathematical decision modeling towards this goal is presented which has the potential for coupling with machine learning (ML) and artificial intelligence (AI) based techniques for rapid screening of the decision variables space.