Introducing the Scalable Anomaly Detection with UC2B framework, this paper addresses the critical task of identifying unusual patterns in data, crucial for effective cyber threat defense. By leveraging ensemble learning methods and the parallel processing capabilities of the Unite and Conquer approach, the framework demonstrates its proficiency in handling large datasets. It strives to offer computational efficiency, scalability, and high accuracy in real-world applications. Notably, this paper places special emphasis on the diversity of components and acknowledges their substantial influence on the overall framework functionality. It encompasses features such as fault tolerance, adaptability to various architectures, and efficient load balancing. Experimental validation on the Ruche Cluster within the realm of cybersecurity provides valuable insights into its potential in detecting anomalies.