Although substantial progress in safety management performance has been made in the construction industry, continuing fatalities and injuries at workplaces hinder sustainable development of this labor-intensive industry. Many machine learning approaches using different types of data such as text, image, video, and audio were adopted for safety risk analysis at construction sites. Our paper aimed to implement a hybrid review of construction safety research based upon machine learning. This hybrid review focused on various attributes from three perspectives: Nature of dataset, machine learning approach, and research topic. After the review of individual attributes, intra-relationships between attributes in each perspective and inter-relationships between attributes across the three perspectives were determined. According to risk recognition, risk prediction, and risk control, feasible research paths were developed from both intra-relationships and inter-relationships between multiple attributes for reference in future studies. Finally, gaps and opportunities were discussed in detail for research agendas on this subject. This hybrid review contributes to outlining the framework of construction safety management based upon machine learning. It is able to provide new entrants with a systematic idea of promising research trends for the future. Research findings are helpful for academia and industry to fill in the gaps between study and practice in the area of construction safety, in order to assist in sustainable development of the construction industry by use of machine learning.