The increasing uncertainty in the manufacturing industry, driven by disruptive events and market fluctuations, highlights the need for more agile and resilient Production Planning and Control (PPC) strategies. This research focuses on addressing the Capacitated Lot Sizing and Scheduling (CLSS) for medium-term planning and Dynamic Scheduling (DS) for short-term decision-making by integrating Mathematical Programming, Artificial Intelligence (AI), and Smart Manufacturing (SM) technologies. A systematic review of CLSS and DS models identifies gaps in modeling uncertainty and adapting to real-time disruptions. A conceptual framework is proposed to standardize CLSS characteristics, while a matheuristic approach combines metaheuristics and mathematical programming to optimize sustainability-driven lot sizing decisions. Additionally, an AI-based tool dynamically reschedules production in response to real-time factory data. The research further develops a scalable architecture for industrial implementation, leveraging IoT and AI for enhanced decision-making. Validation is conducted through real and synthetic case studies, assessing solution quality and computational efficiency. This work aims to bridge the gap between traditional optimization techniques and smart, data-driven SM environments.