The chemical industry faces unprecedented pressure to enhance operational efficiency while simultaneously reducing environmental impact and meeting stringent regulatory requirements. This article presents a comprehensive framework for integrating Artificial Intelligence (AI), Digital Twins (DT), and Advanced Process Control (APC) to achieve sustainable and efficient chemical manufacturing. The proposed methodology leverages AI-driven predictive models, including neural networks and reinforcement learning, to enable real-time optimization of complex nonlinear processes. Digital twins serve as the central integration platform, providing a continuously evolving virtual representation of physical systems that fuses process data, mechanistic models, and domain knowledge. The framework incorporates a knowledge graph-based semantic architecture that ensures interoperability, scalability, and adherence to FAIR principles for data and model management. Advanced process control strategies, augmented by AI agents, enable autonomous decision-making and adaptive control under varying operational conditions. A case study on a commercial-scale BTX recovery process demonstrates the system's effectiveness, achieving a 28.52% improvement in economic performance, 79.45% reduction in emissions, and 15.96% decrease in carbon footprint while maintaining strict regulatory compliance. The results validate that the synergistic integration of these technologies creates a paradigm shift toward self-optimizing, resilient, and sustainable chemical manufacturing systems.