With the increase in musculoskeletal injuries caused by poor posture and excessive physical exertion at work, assistive wearable solutions such as occupational exoskeletons have been developed. These exoskeletons are paired with advanced wearable sensors that monitor physical exertion and provide real-time data on muscle activity and movement trajectory. Exoskeletons help users by improving their posture and enabling a more effective redistribution of the load on the working muscles. In recent years, there has been a significant increase in efforts to create innovative tools and methods that incorporate machine learning (ML) systems and sensor technologies into the risk assessment prediction of exoskeletons. The ML systems process the data that sensors collect to enhance the accuracy of risk assessments. For example, electromyography (EMG) sensors have been used in previous studies of exoskeletons to measure muscle activation levels and muscle strain and fatigue during various manual tasks. The primary objective of this poster is to discuss the effectiveness of existing ML systems, which aid user training in exoskeleton research and predict risk assessments of industrial exoskeletons. The current systems have shown substantial benefits such as accurately predicting risk for a certain muscle group while carrying out a particular action. However, there is a significant limitation in the scope of these ML models because of a lack in experimented data. In order to compensate, many of the ML models extensively used data augmentation which hinders the system’s overall accuracy. Studies have shown that integrating more sophisticated sensors with real-time insights can help reduce the reliance on data augmentation by providing real-world and immediate data. The poster intends to considerably aid future developments by presenting a comprehensive outline of sensor-integrated ML models in risk assessors of occupational exoskeletons.