Semiconductor manufacturing is becoming increasingly complex due to the continuous scaling of device dimensions, stringent quality requirements, and the demand for high production efficiency. Traditional manufacturing approaches often face challenges related to process variability, equipment failures, defect detection, and yield optimization, limiting their ability to meet the requirements of next-generation integrated circuits. Artificial Intelligence (AI) has emerged as a transformative technology that enables intelligent automation, predictive analytics, and real-time decision-making throughout semiconductor fabrication processes. This paper presents an AI-driven semiconductor manufacturing framework that integrates machine learning, deep learning, computer vision, edge computing, Industrial Internet of Things (IIoT), and digital twin technologies to enhance wafer fabrication, process monitoring, equipment maintenance, and quality inspection. The proposed framework employs deep neural networks for defect classification, reinforcement learning for adaptive process optimization, predictive maintenance models for equipment health monitoring, and digital twins for virtual process simulation and continuous optimization. Furthermore, AI-enabled analytics facilitate anomaly detection, resource allocation, and production scheduling while reducing manufacturing costs and energy consumption. The integration of explainable AI techniques improves transparency and supports informed decision-making in high-precision fabrication environments. The proposed approach is expected to improve manufacturing yield, reduce defect rates, minimize equipment downtime, optimize resource utilization, and enhance overall production efficiency, thereby supporting the development of intelligent, resilient, and sustainable semiconductor manufacturing systems aligned with Industry 4.0 and smart factory initiatives.