Intelligent network orchestration and management are crucial elements in the future of Sixth-Generation (6G) networks, although the cloudification of microservices-oriented networks is a well-established aspect of Fifth-Generation (5G) systems. Consequently, the envisioned 6G framework heavily depends on artificial intelligence (AI), machine learning (ML), and deep learning (DL) to enhance network security. Ensuring end-to-end authentication in future networks necessitates proactive threat detection, innovative mitigation strategies, and the self-sufficiency of 6G networks. This paper explores the potential applications of AI in enhancing the security of 6G body area networks. Specifically, it introduces an innovative anomaly detection system tailored for 6G body area networks, leveraging group learning within communication networks. The process begins with pre-processing, followed by a feature selection strategy that compares ensemble learning and feature selection to implement a reimagined hybrid approach. Dimensionality reduction is applied to three datasets—UNSW_NB2015, CIC_IDS2017, and NSL KDD—to identify the most relevant feature subsets for each. In the final stage, hybrid Ensemble Learning (EL) techniques are used for intrusion detection, employing a modified version of the random forests (RF) classifier within the Adaboost.M1 algorithm, with average voting as the aggregation method. Both binary and multi-class classification approaches are used to validate the system’s effectiveness.