The Zero-touch network and Service Management (ZSM) framework represents an emerging paradigm in the management of the fifth-generation (5G) and Beyond (5G+) networks, offering automated self-management and self-healing capabilities to address the escalating complexity and the growing data volume of modern networks. ZSM frameworks leverage advanced technologies such as Machine Learning (ML) to enable intelligent decision-making and reduce human intervention. This paper presents a comprehensive survey of Zero-Touch Networks (ZTNs) within the ZSM framework, covering network optimization, traffic monitoring, energy efficiency, and security aspects of next-generational networks. The paper explores the challenges associated with ZSM, particularly those related to ML, which necessitate the need to explore diverse network automation solutions. In this context, the study investigates the application of Automated ML (AutoML) in ZTNs, to reduce network management costs and enhance performance. AutoML automates the selection and tuning process of a ML model for a given task. Specifically, the focus is on AutoML's ability to predict application throughput and autonomously adapt to data drift. Experimental results demonstrate the superiority of the proposed AutoML pipeline over traditional ML in terms of prediction accuracy. Integrating AutoML and ZSM concepts significantly reduces network configuration and management efforts, allowing operators to allocate more time and resources to other important tasks. The paper also provides a high-level 5G system architecture incorporating AutoML and ZSM concepts. This research highlights the potential of ZTNs and AutoML to revolutionize the management of 5G+ networks, enabling automated decision-making and empowering network operators to achieve higher efficiency, improved performance, and enhanced user experience.

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