The FMCG (fast-moving consumer goods) business is challenged with increasing requirements for production efficiency, equipment reliability, product safety, and sustainability in shop floors characterized by fast-moving and changing production lines. In the past, Lean Manufacturing and Total Productive Maintenance (TPM) have been used to drive process improvement, but these approaches are designed without real-time data (key to advances in Industry 4.0). When we combine AI/ML with Lean TPM, it has transformative possibilities: it can allow us to do predictive maintenance, advanced anomaly detection, and deploy cyber-physical systems. This study distills the merits of AI augmentation and resultant frameworks and its implementation challenges for Lean TPM in FMCG processes. Based on cross-sector case studies and extensive literature, the results show that AI application assists in equipment efficiency, fault diagnosis, and dynamic scheduling, which results in an increase in OEE as well as sustainability performance. However, obstacles including data interoperability difficulties, skills strain, SME uptake challenges, and lack of standardized benchmarking continue to limit wider adoption. We present a conceptual integration model and pragmatist application approach to support stakeholders and academics as they negotiate this new terrain. The latter is set to take maintenance paradigms further from “reactive” into “proactive,” and eventually “condition-based” and “predictive” with a focus on the data-driven framework that is necessary to build up industry resilience and competitiveness in the digital era.