This study builds a data-based predictive model to identify the factors that affect the sustainability of faculty in Saudi universities and correlates these with the indicators of quality assurance and institutional governance based on Saudi Vision 2030. The recent surge in quality accreditation in Saudi higher education has yielded tangible positive returns in education sector, but this has also led to increased teaching and administrative workloads. This disparity can adversely affect job satisfaction, lead to burnout and high attrition, and may eventually diminish markers of academic excellence that accreditation systems are supposed to foster. This study uses multivariate statistics (exploratory factor analysis, multiple and logistic regression, and Structural Equation Modeling (SEM)) to derive indicators of performance and a usable predictive model of faculty sustainability. It utilizes quantitative data collected from one hundred faculty members across Saudi universities through a standardized, validated questionnaire to form a baseline, complemented with institutional human resource records. Based on empirical evidence, the four most significant predictors of faculty sustainability are teaching workload intensity, institutional support, perceived organizational justice, and readiness to undergo digital transformation. The research suggests a set of practical performance metrics in accordance with NCAAA standards of accreditation along with a conceptual decision-support dashboard. The study has implications for policymakers in Saudi higher education who aim to achieve a sustainable balance between quality demands and human capital welfare, in tandem with the objectives of quality education, governance, and data-based institutional management stated in the Vision 2030 document.