In the contemporary mechanical engineering plants, unexpected equipment breakdown and ineffective management of the workflow are central participants in the change of productivity, operational costs and the reliability of the system. Traditional maintenance strategies, reactive and scheduled maintenance, are not adequate in dealing with dynamic industrial issues and they tend to lead to more downtime, and resource inefficiency. To address these shortcomings, this paper will present a proposal of an AI enabled operations control model that incorporates the predictive maintenance, workflow optimization, and intelligent decision support into a single framework. The given model uses the Industrial Internet of Things (IIoT) sensors to monitor real-time data and uses the machine learning and deep learning algorithms, such as Random Forest, XGBoost, LSTM, and CNN, to forecast machine malfunctions and determine the health status of equipment. In addition, to optimize scheduling and resource allocation using predictive understanding, there is a workflow optimization engine to improve on the same. The system is closed-loop architecture making it possible to monitor, make adaptive decisions and control operations in real time. Through the experimental assessment, the proposed model is shown to be very predictive and causes significant reduction of machine downtime and enhances the efficiency of the workflow and use of resources. The comparative analysis between the traditional maintenance methods and that offered by the AI-based model indicates that the latter has a better reliability, responsiveness, and productivity. The suggested structure offers a scalable and intelligent solution to smart manufacturing buildings, which will help in the development of Industry 4.0 and the sphere of industrial automation of the next generation.