This paper introduces the Socio-Technical Maintenance Equity Framework (STMEF), a normative and operational model for enhancing data-driven decision-making in facility management. Existing Computer-Aided Facility Management (CAFM) systems predominantly rely on asset-centric indicators, often overlooking the socio-cultural dimensions that shape building use, maintenance behavior, and service equity. Drawing on interdisciplinary insights and a large-scale CAFM dataset from Denmark, the framework integrates technical maintenance data with contextual indicators, such as task diversity, trade recurrence, and access failures, and simulates the incorporation of public socio-demographic attributes. The framework enables predictive, equity-aware prioritization while embedding ethical safeguards, feedback mechanisms, and participatory governance. The framework is tested through spatial load profiling, cross-trade clustering, and scenario-based analysis. Findings demonstrate that facility management systems can be adapted to recognize buildings as socio-technical environments and to support context-aware interventions that improve both performance and fairness. The paper contributes an approach to aligning predictive maintenance with the lived realities of occupants.