<p>AutoML represents a pivotal advancement in machine learning by simplifying and speeding model development. This paper provides a comprehensive survey of AutoML, tracing its evolution from early metalearning, hyperparameter optimization, and transfer learning techniques to the latest advancements in neural architecture search, automated pipeline design, and few-shot learning. It covers historical context, classical approaches, and modern applications while also addressing emerging topics. Key research directions are highlighted, focusing on enhancing model interpretability, improving generalization and robustness, expanding automated pipeline design, and ethical implications of AutoML technologies. This paper aims to provide a holistic view of the current state of AutoML, serving as a valuable resource for researchers, practitioners, and stakeholders seeking to understand and advance the capabilities of AutoML in both theoretical and practical contexts.</p>