Hybrid manufacturing systems integrate additive, subtractive, and conventional manufacturing operations within interconnected production environments. While such integration provides opportunities for complex geometries, material efficiency, process flexibility, and improved product functionality, it also creates substantial quality-control challenges because multiple process mechanisms contribute to the final product simultaneously. This research and review paper examines industrial quality control in hybrid manufacturing systems through a structured synthesis of the provided literature. Particular attention is given to additive manufacturing characteristics, hybrid process integration, data-driven manufacturing, digital twins, lean methodologies, sustainability, and supplier-performance evaluation. The review identifies that quality assurance in hybrid manufacturing cannot be adequately addressed through isolated inspection after production; instead, it requires an integrated framework combining process monitoring, dimensional verification, material characterization, data analytics, traceability, and continuous improvement. The analysis further indicates that additive manufacturing introduces process-specific variability that must be incorporated into broader hybrid quality systems (Srivastava & Rathee, 2022). Digital and data-driven approaches provide mechanisms for connecting production data with quality decisions, while lean principles support systematic reduction of process variation and non-value-adding activities. A conceptual industrial quality-control framework is therefore proposed, consisting of input qualification, process integration, real-time monitoring, adaptive control, inspection, data-driven evaluation, and continuous improvement. The study contributes a consolidated perspective for designing quality systems capable of addressing the technical and organizational complexity of hybrid manufacturing.