Sinkhole attacks pose a severe threat to network security, disrupting communication, exposing sensitive data, and enabling denial-of-service (DoS) attacks. Traditional detection methods, such as signature-based and anomaly-based techniques, fall short in identifying novel and sophisticated sinkhole attacks. Machine learning (ML) emerges as a promising approach for sinkhole detection due to its ability to learn from network traffic patterns and identify subtle indicators of malicious activity. This paper explores the domain of ML-based sinkhole detection, providing a comprehensive overview of the various ML techniques employed and their effectiveness in combating sinkhole attacks. It delves into supervised learning algorithms, such as decision trees, support vector machines (SVMs), and neural networks, which can be trained on labeled data to classify network traffic as either normal or malicious. Additionally, unsupervised learning algorithms, such as k-means clustering and anomaly detection models, are discussed for their ability to identify outliers and deviations from normal network behavior. The paper highlights the advantages of ML-based sinkhole detection, emphasizing its ability to adapt to new attack patterns and identify previously unknown sinkholes. It also addresses the challenges associated with ML-based approaches, including the need for large training datasets, computational complexity, and potential for false positives.