Industry 5.0 smart manufacturing environments integrate connected machines, intelligent analytics, autonomous decision-making, human-machine collaboration, and increasingly heterogeneous data-driven services. This integration improves operational intelligence but also creates an environment in which previously unseen cyberattacks can propagate across interconnected industrial assets before conventional intrusion detection systems have sufficient evidence to recognize them. This paper proposes a conceptual Adaptive Meta-Learning Framework for Zero-Day Attack Detection (AML-ZD) designed for Industry 5.0 smart manufacturing environments. The framework combines meta-learning, adaptive representation learning, anomaly detection, contextual feature modeling, and ensemble-based decision mechanisms to identify attack behaviors that differ substantially from previously observed attack classes. The methodological foundation is informed by the provided literature on machine learning, deep learning, self-attention, personalization, and adaptive recommendation systems, while the Industry 5.0 security positioning is anchored particularly by the hybrid ensemble perspective of Govindarajan et al. (2026). The paper argues that the transfer-learning and adaptation principles visible across the supplied machine-learning literature can be reinterpreted for cybersecurity, where models must generalize beyond known attack signatures. The resulting framework emphasizes rapid adaptation, behavioral representations, uncertainty awareness, and continuous learning rather than dependence on fixed attack labels. Analytical findings indicate that adaptive meta-learning is theoretically better aligned with zero-day detection than static supervised classification, although its effectiveness depends on representative industrial telemetry, robust adaptation mechanisms, careful threshold calibration, and protection against poisoned or misleading adaptation data. The paper concludes that meta-learning should function as an adaptive layer within a broader industrial intrusion-detection architecture rather than as an isolated classifier.