Predictive Maintenance (PdM) is a key area of smart manufacturing and it relies on incorporating the technologies from Industry 4.0, such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML). This thesis will present the literature and most advanced concepts related to PdM following by the application studies by leading companies in the world in the field. PdM effectively improves efficiency as it can accurately forecast machine breakdowns well in advance, resulting in reduced production downtime and maintenance costs. More sophisticated methods, such as deep learning, Bayesian filtering, or reinforcement learning, can further improve the accuracy of the prediction, and digital twins, edge computing allow for real-time decision making. However, there are challenges with the implementation of PdM, such as integration with existing systems, poor data quality and implementation cost, which are relatively high for small and medium-sized businesses (SMEs). However, industry adopters like Bosch, GE Aviation and Siemens talk of demonstrable improvements: emissions lowered, energy use reduced and equipment life extended. Bottom Line: PdM is not a choice, it’s a necessity for the future of manufacturing and PdM’s value is being able to predict and prevent equipment failures. This thesis ends with practical guidelines and strategic outlooks to provide support for practitioners and academics to deploy PdM for sustained competitiveness and innovation