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Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI\/ML techniques used for CHA, including conventional\u00a0supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of\u00a0data acquisition and\u00a0the benchmark datasets. Finally, we discuss open issues\u00a0and challenges in using AI and ML for CHA along with some\u00a0possible solutions. In summary, this paper presents CHA tools, lists\u00a0various data\u00a0acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. 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