Continuous manufacturing is transforming pharmaceutical production by replacing segmented batch operations with integrated, dynamic process trains, which necessitates quality assurance tools that operate continuously rather than relying primarily on end-product release testing. Current control strategies often interpret PAT measurements as isolated trends rather than as part of a connected process state, limiting their ability to anticipate how interacting material attributes, process parameters, and sensor signals shape final product quality. To address this, a digital-twin framework is proposed that ingests multivariate PAT signals, updates a hybrid predictive model, and continuously estimates critical quality attributes in real time, supporting proactive control and real-time release decision-making. The framework integrates a PAT data fusion module, a hybrid physics-informed machine-learning predictor, a digital-twin state estimator, a model drift monitor, and an MES-integrated control advisor, operating as a regulated decision-support architecture rather than an autonomous release mechanism. By enhancing visibility into evolving process quality, enabling early detection of deviations, and helping operators evaluate corrective actions before quality risk becomes material, this approach could also strengthen documentation for real-time release testing through traceable links between sensor data, model predictions, and control recommendations. Overall, an integrated digital-twin framework has the potential to advance the reliability and efficiency of continuous pharmaceutical manufacturing, provided it is implemented with rigorous validation, disciplined model lifecycle management, and alignment with regulatory expectations for predictive quality systems.