Integrating the Internet of Things (IoT) and Machine Learning (ML) within smart manufacturing facilities has significantly transformed anomaly detection processes, thereby ensuring predictive maintenance, optimizing processes, and enhancing operational efficiency. This paper reviews existing research regarding real‑‑time anomaly detection in IoT‑enabled manufacturing environments, specifically emphasizing biopharmaceutical production. A variety of Machine Learning (ML) techniques, including convolutional neural networks (CNNs), hidden Markov models (HMMs), and statistical methodologies, are examined to identify deviations from standard operating conditions. The research identifies distinct challenges associated with anomaly detection, including issues related to data collection errors, class imbalance, and constraints in the selection of ML models, which can potentially impact the accuracy of predictions. Furthermore, the paper delineates a systematic methodology for integrating anomaly detection in biopharmaceutical Active Pharmaceutical Ingredient (API) manufacturing, highlighting the importance of infrastructure assessment, data acquisition, model training, and ongoing improvement. The findings accentuate the necessity of bridging the gap between Information Technology (IT) and Operational Technology (OT), securing IoT networks, and enhancing ML models to mitigate false positives and false negatives. Ultimately, this study advances data‑driven decision‑making in smart manufacturing, promoting a more robust and resilient industrial ecosystem.