Automated Machine Learning (AutoML) is a rapidly growing research area aimed at making high-performance machine learning techniques accessible to a broad set of users. This article provides an extensive overview of the past, present, and future perspectives of AutoML. It introduces the concept of AutoML, defines the problems it aims to solve, and describes the three underlying components: the search space, search strategy, and performance evaluation. The article discusses hyperparameter optimization (HPO) techniques, neural architecture search (NAS), and reviews available AutoML systems. It also highlights open challenges and future research directions. Overall, the survey offers a comprehensive overview for researchers and practitioners in the field of machine learning, providing a basis for further developments in AutoML.Automated Machine Learning (AutoML) is a rapidly growing research area aimed at making high-performance machine learning techniques accessible to a broad set of users. This article provides an extensive overview of the past, present, and future perspectives of AutoML. It introduces the concept of AutoML, defines the problems it aims to solve, and describes the three underlying components: the search space, search strategy, and performance evaluation. The article discusses hyperparameter optimization (HPO) techniques, neural architecture search (NAS), and reviews available AutoML systems. It also highlights open challenges and future research directions. Overall, the survey offers a comprehensive overview for researchers and practitioners in the field of machine learning, providing a basis for further developments in AutoML.