The condition assessment of equipment in nuclear power plants (NPPs) could provide essential information for operation and maintenance decisions, which would have a significant impact on improving the safety and economy of NPPs. To date, substantial work has been conducted on the condition assessment based on machine learning for NPP equipment. To provide a comprehensive overview for researchers interested in developing machine learning methods for NPP equipment condition assessment, this critical review presents a detailed literature survey on state-of-the-art research and identifies challenges for future study. Valuable information is presented, including major failure modes, data sources, maintenance strategies, and the relationship between equipment lifetime, assessment technology, and maintenance strategy. Following the typical lifetime of NPP equipment for condition assessment, current works in this domain are categorized into anomaly detection, remaining useful life prediction, and fault detection and diagnosis. The techniques and methodologies adopted in the literature are summarized from each aspect. In particular, the in-depth NPP equipment condition assessment survey based on deep learning methods is presented. In addition, we elaborate on current issues, challenges, and future research directions for the condition assessment of equipment in NPPs. These directions we believe will pave the way for equipment condition assessment.