The increasing complexity of modern manufacturing systems challenges the coordination of production planning and predictive maintenance. While AI-driven optimization has enhanced individual manufacturing processes, a lack of integration between predictive maintenance, production scheduling, and real-time factory conditions leads to inefficiencies, downtime, and suboptimal decision-making. This research proposes a conceptual framework that addresses three key gaps: (i) integrating prognostic models with logistical metrics in predictive maintenance, (ii) unifying dynamic planning for production and maintenance, and (iii) adapting decision-making methods to changing industrial scenarios. The proposed framework is structured into three layers: a simulation platform for performance evaluation, a meta-learning system for adaptive prognostic modeling, and a reinforcement learning module for real-time optimization. Additionally, a Service-Oriented Architecture (SOA) and ontologies ensure data integration, management, and interoperability. This approach enhances operational efficiency by minimizing downtime, optimizing resource allocation, and improving process predictability. By advancing intelligent manufacturing, this research aligns with Industry 4.0's vision of flexible, integrated, and autonomous production systems.