Against the backdrop of rapid industrialization and urbanization, the demand for hoisting and transportation of large mechanical equipment is constantly increasing. How to effectively improve the safety and efficiency of these operations has become a core challenge in the engineering field. This article optimizes the workflow and supports decision-making through real-time data monitoring and analysis. Sensors can be used to collect dynamic data during hoisting and transportation processes, combined with machine learning algorithms to process and analyze this data, to establish an intelligent simulation model to predict potential risks and optimize operational strategies. The system first monitors the status and working environment of the lifting equipment in real-time through a data acquisition module, such as key parameters such as lifting speed, angle, wind speed, etc. Then, data preprocessing techniques are used to clean and integrate data, ensuring the accuracy of subsequent analysis data. In the data analysis stage, this article combines statistical analysis and machine learning techniques (such as decision trees and random forests) to analyze data features and identify key factors that affect lifting safety and efficiency. The system performs particularly well, with risk prediction accuracy exceeding 95% and even reaching 98% in extreme wind speed environments, based on these analysis results. The simulation module uses algorithms to simulate different lifting schemes, evaluate their safety and efficiency, and provide scientific decision-making basis for operators.