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Health inequities persist as a critical challenge in modern healthcare systems, often manifesting through unequal access to essential services. This conceptual manuscript introduces a novel disparity surveillance framework designed for near-real-time detection of health inequities in service access monitoring. By integrating artificial intelligence-driven analytics with infrastructural orchestration, the framework emphasizes proactive identification of access disparities across diverse populations. Drawing from theoretical foundations in public health equity and AI governance, we propose the near-real-time inequity monitoring architecture (NRIMA). This layered system incorporates data ingestion, disparity analytics, and adaptive feedback mechanisms to enhance surveillance efficacy. Without relying on empirical data or model training, the architecture focuses on theoretical constructs such as risk propagation models and decision confidence formulas to interpret potential inequities. Key components include modular layers for real-time signal processing and governance-compliant orchestration, ensuring ethical deployment in clinical and community settings. The framework’s unique feedback topology promotes dynamic adjustments to monitoring protocols, mitigating biases in service allocation. Through literature synthesis, we highlight alignments with existing AI applications in health surveillance while advancing conceptual uniqueness. Ultimately, this work contributes to theoretical discourse on AI-enabled equity in healthcare, advocating for infrastructural innovations that prioritize inclusivity and timeliness in disparity detection.