This survey investigates anomaly detection techniques in blockchain ecosystems using unsupervised learning. Blockchain networks are decentralized systems that maintain an expanding ledger of transaction information, enabling secure and effective data storage and analysis across various domains. Anomalies in blockchain networks can arise from various threats, including double-spending, Sybil attacks, 51% attacks, and malicious activities such as phishing, Ponzi schemes, and cryptojacking. Anomaly detection aims to identify and quantify these anomalies, which may manifest as suspicious transactions or user behaviors. Unsupervised learning is increasingly used in blockchain anomaly detection due to its ability to segment data into distinct groups and identify outliers. The survey categorizes various unsupervised learning algorithms, including partitional methods, graph-based methods, density-based approaches, probabilistic models, one-class classification, tree-based methods, and dimensionality reduction techniques. These algorithms are evaluated based on their characteristics, strengths, and weaknesses, and their applicability in different scenarios. The survey also discusses the challenges and future directions in blockchain anomaly detection, emphasizing the need for efficient data structures and the integration of unsupervised learning with supervised and self-supervised approaches. The analysis highlights the importance of data formatting and structure in the implementation of unsupervised learning methods, as well as the need for robust and scalable solutions to address the complexities of blockchain networks. The survey concludes with a comprehensive overview of the current state of research and the potential for future advancements in blockchain anomaly detection.This survey investigates anomaly detection techniques in blockchain ecosystems using unsupervised learning. Blockchain networks are decentralized systems that maintain an expanding ledger of transaction information, enabling secure and effective data storage and analysis across various domains. Anomalies in blockchain networks can arise from various threats, including double-spending, Sybil attacks, 51% attacks, and malicious activities such as phishing, Ponzi schemes, and cryptojacking. Anomaly detection aims to identify and quantify these anomalies, which may manifest as suspicious transactions or user behaviors. Unsupervised learning is increasingly used in blockchain anomaly detection due to its ability to segment data into distinct groups and identify outliers. The survey categorizes various unsupervised learning algorithms, including partitional methods, graph-based methods, density-based approaches, probabilistic models, one-class classification, tree-based methods, and dimensionality reduction techniques. These algorithms are evaluated based on their characteristics, strengths, and weaknesses, and their applicability in different scenarios. The survey also discusses the challenges and future directions in blockchain anomaly detection, emphasizing the need for efficient data structures and the integration of unsupervised learning with supervised and self-supervised approaches. The analysis highlights the importance of data formatting and structure in the implementation of unsupervised learning methods, as well as the need for robust and scalable solutions to address the complexities of blockchain networks. The survey concludes with a comprehensive overview of the current state of research and the potential for future advancements in blockchain anomaly detection.