Unexpected equipment breakdowns continue to cause major disruptions and financial losses across manufacturing industries. In tyre production facilities, unplanned failures of critical machinery components lead to significant downtime and reduced productivity. Traditional maintenance approaches—reactive, preventive, or condition-based—often fail to balance reliability and cost efficiency. Predictive maintenance (PdM), enhanced by machine learning (ML), provides a robust solution by forecasting equipment degradation and enabling proactive intervention. This paper reviews the evolution of maintenance strategies and the transformative role of data-driven models, including Artificial Neural Networks, Support Vector Machines, Random Forests, and Gradient Boosting methods. It contrasts these adaptive techniques with deterministic mathematical models such as Markov processes and Weibull analysis. Emphasis is placed on tyre manufacturing applications, highlighting key challenges in data acquisition, model interpretability, and real-time implementation. The study concludes that ML-integrated PdM frameworks can substantially reduce downtime, improve maintenance scheduling, and extend equipment life—positioning machine learning as a vital enabler of smart, reliable, and efficient manufacturing within the Industry 4.0 landscape.