The efficient and safe operation of long heavy cranes critically depends on the precise identification and control of the hook's movement. A comprehensive study is presented on the identification and control mechanisms for the hook in long heavy cranes, focusing on minimizing oscillations and improving positional accuracy. Advanced sensor fusion techniques are utilized, integrating data from accelerometers, gyroscopes, and position encoders to develop a robust real-time model of the hook dynamics. A state-of-the-art control algorithm, based on Model Predictive Control (MPC), is implemented to regulate the hook's position and mitigate swing. Extensive simulations and real- world experiments validate the proposed approach, demonstrating significant improvements in hook stabilization and load handling efficiency. The results highlight the potential for enhanced operational safety and productivity in crane operations. This research contributes to the field of crane automation by providing a scalable and effective solution for hook control in industrial applications.