The fast growth of the Industrial Internet of Things (IIoT) has changed modern manufacturing by allowing real-time data collection, smart automation, and predictive analysis. However, the large amount of data from industrial sensors and machines needs efficient processing, analysis, and decision-making to improve efficiency and reduce downtime. This study introduces an Intelligent Industrial IoT (I-IIoT) system that combines edge computing, artificial intelligence (AI), and big data analysis to support smart manufacturing and predictive maintenance. The proposed model uses machine learning (ML) and deep learning (DL) to identify equipment issues, predict failures, and improve production. A cloud-edge hybrid setup processes data in real time, reducing delays and making the system more responsive. A blockchain-based data-sharing method ensures data security, privacy, and smooth communication between IIoT systems. Comparisons with traditional maintenance methods reveal significant improvements, such as over 95% accuracy in predictions, a 30% reduction in downtime, and 25% better use of resources. These results suggest that data-driven IIoT solutions can transform industrial operations by improving automation, security, and decision-making, leading to the next level of smart factories.