As the complexity and scale of oilfield development continue to grow, equipment condition monitoring and fault prediction play a crucial role in ensuring production safety and enhancing operational efficiency. In recent years, significant progress has been made in applying big data and artificial intelligence (AI) technologies in this field, enabling more accurate condition assessment and fault warning capabilities. This paper systematically reviews the latest research advances in oilfield equipment condition monitoring and fault prediction, focusing on key methods, implementation processes, and real-world application cases based on big data and AI technologies. Specifically, it explores the core principles of condition monitoring, AI-driven predictive models, and the performance of related technologies in industrial scenarios. Moreover, it provides an in-depth analysis of current challenges, including data quality issues, the generalization ability of AI models, limitations in real-time performance, and interpretability. It also discusses future development trends, such as integrating edge computing and digital twin technologies to enhance system intelligence. By thoroughly summarizing existing achievements and challenges, this paper provides theoretical foundations and technical references for further research and optimization of oilfield equipment monitoring and fault prediction systems.