Automated Machine Learning (AutoML) plays a pivotal role in making machine learning more accessible by automating key steps in the model development process. Over the past decade, an increasing number of literature reviews (LRs) have examined specific components of AutoML, including data preparation, feature engineering, model generation, neural architecture search, hyperparameter optimization, and evaluation. However, a unified synthesis that consolidates findings across these reviews is missing. This paper presents a tertiary review of 32 LRs to provide a comprehensive and up-to-date overview of the AutoML landscape. We systematically analyze the core AutoML phases, categorize AutoML methods used across different stages of the machine learning (ML) pipeline, and compile a set of AutoML frameworks and tools. The synthesis offers a panoramic view of the techniques and tools supporting automation across ML phases. Our findings aim to serve as a reference for researchers and practitioners seeking to understand the current state of AutoML, the extent of automation achieved across pipeline stages, and the tools and platforms that support these capabilities.