This study assessed a novel statistical approach using space-filling designs (SFDs) and self-validating ensemble modeling (SVEM) machine learning to efficiently identify key process factors using recombinant adeno-associated virus type 9 (rAAV9) gene therapy manufacturing as a case study. Based on risk assessment of parameters that may impact rAAV9 production, we have evaluated six process parameters using 24-run SFDs generated by the JMP statistical software. SFDs are a new class of design of experiment (DoE) created with the objective of covering the entire design space as completely as possible; this in turn allows more accurate modeling of complex response surface behavior typically found in bioprocesses.