Real-time release testing promises faster and more robust quality assurance for pharmaceutical tablets by shifting quality assessment from delayed laboratory testing to continuous process understanding. Current PAT models, however, often treat chemical spectra and manufacturing signals as separate information streams rather than parts of one integrated quality system. Existing RTRT models are frequently centered on spectroscopy for chemical CQAs or on threshold-based monitoring of tablet press behavior. This separation limits the ability to capture multivariate interactions among formulation chemistry, compression behavior, and final tablet performance. This article proposes an AI architecture that ingests Raman spectra and tablet press signals in real time, fuses them within a multimodal model, and outputs a comprehensive quality statement. The architecture is intended to support predictions for assay, content uniformity, hardness, and dissolution as part of real-time batch release decisions. The proposed system includes a Raman preprocessing and chemometric feature extractor, a tablet press signal encoder, a multimodal fusion layer, and a multi-head quality predictor. It also includes a decision-support module with uncertainty handling and a model-monitoring layer for detecting drift and sensor degradation. Such an architecture would provide a holistic quality assessment of tablets during manufacture rather than after batch completion. It could support a transition from laboratory-centered release to in-line, evidence-based release decisions within regulated manufacturing systems. An AI-driven, multivariable RTRT system could transform tablet manufacturing from a batch-tested process to a continuously assured, data-driven quality model. Its value would depend on robust validation, lifecycle management, and alignment with pharmaceutical quality systems.