This paper provides a comprehensive survey of anomaly detection techniques in blockchain ecosystems using unsupervised learning. It explores the intricate nature of abnormal behaviors and examines advanced algorithms to identify deviations from normal patterns. The authors propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed as both an implementation and integration procedure, where the merits of these algorithms can be effectively combined based on the specific problem at hand. The main contributions of the paper include a thorough study of the interplay between various unsupervised learning algorithms and their application in detecting malicious activities within public and private blockchain networks. The paper also categorizes research methods into three categories based on their implementation strategies and presents the basic functional properties of these categories. Additionally, it discusses typical anomalies in blockchain networks and the data structures commonly used in unsupervised learning-based anomaly detection. The paper highlights challenges and future directions for research in this field. The authors searched multiple databases and identified a significant increase in papers dealing with anomaly detection in blockchain from 2018 onwards. The paper is structured into several sections, including an introduction, related work, blockchain overview, anomalies and anomaly detection in blockchain, categories of unsupervised learning algorithms, perspectives on supervised and self-supervised approaches, evaluation approaches, and data structures used in blockchain anomaly detection.This paper provides a comprehensive survey of anomaly detection techniques in blockchain ecosystems using unsupervised learning. It explores the intricate nature of abnormal behaviors and examines advanced algorithms to identify deviations from normal patterns. The authors propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed as both an implementation and integration procedure, where the merits of these algorithms can be effectively combined based on the specific problem at hand. The main contributions of the paper include a thorough study of the interplay between various unsupervised learning algorithms and their application in detecting malicious activities within public and private blockchain networks. The paper also categorizes research methods into three categories based on their implementation strategies and presents the basic functional properties of these categories. Additionally, it discusses typical anomalies in blockchain networks and the data structures commonly used in unsupervised learning-based anomaly detection. The paper highlights challenges and future directions for research in this field. The authors searched multiple databases and identified a significant increase in papers dealing with anomaly detection in blockchain from 2018 onwards. The paper is structured into several sections, including an introduction, related work, blockchain overview, anomalies and anomaly detection in blockchain, categories of unsupervised learning algorithms, perspectives on supervised and self-supervised approaches, evaluation approaches, and data structures used in blockchain anomaly detection.