<![CDATA[Crane systems are vital components across multiple industrial sectors, including construction, port logistics, and the maritime sector. Crane operations involving heavy load lifting under dynamic working conditions present high risks of structural failure and overload incidents, which can cause workplace accidents and equipment damage. Advances in digital technology, particularly Internet of Things (IoT), Digital Twin, and machine learning, open new avenues for improving monitoring systems and operational risk mitigation in cranes. This study analyzes the effectiveness of integrating these technologies in strengthening structural health monitoring systems and minimizing overload risks in crane operations. The method employed is a Systematic Literature Review (SLR) examining scientific publications from 2021 to 2025, sourced from databases including IEEE Xplore, ScienceDirect, Springer Link, and Google Scholar. Findings indicate that the integration of smart sensors, wireless communication systems such as LoRa, and machine learning-based analytics improves load monitoring accuracy and enables early warning systems against potential structural failure. Digital Twin technology further supports virtual operational simulation to identify overload risks prior to the lifting process. IoT and artificial intelligence integration therefore plays a central role in improving operational safety and maintenance efficiency of crane systems across various industrial sectors]]>
