Wireless sensor networks (WSNs) are integral components of modern infrastructure, providing pervasive monitoring and data collection capabilities across diverse domains. However, the reliability of WSNs can be compromised by various faults, including sensor failures, communication errors, and environmental disturbances. Traditional fault diagnosis methods often struggle to cope with the dynamic and resource-constrained nature of WSNs. In response, this paper explores the application of machine learning techniques for fault diagnosis in WSNs. We present the challenges associated with fault diagnosis in WSNs and discuss how machine learning algorithms offer promising solutions by leveraging data-driven approaches. The review encompasses various machine learning paradigms, including supervised, unsupervised, and semi-supervised learning, tailored to the unique characteristics and constraints of WSNs. Additionally, we discuss the key considerations and trade-offs involved in selecting and deploying machine learning models for fault diagnosis in WSNs.