The ratio of two normal variables and compositional Data (CoDa) are two common types of process data in industrial and manufacturing applications. Control charts are powerful tools in statistical process control for monitoring these types of data, as they allow for the detection of process changes and improvement in process quality. In this chapter, we provide a comprehensive analysis of the existing literature on control charts for monitoring the ratio of two normal variables and CoDa and offer a perspective on the strengths and limitations of these methods, as well as potential areas for future research. Specifically, the review is organized into two main categories: control charts for monitoring the ratio of two normal variables and control charts for monitoring CoDa. In this comprehensive analysis, we examine 87 research studies, comprising 68 that focus on the ratio of two normal variables and 19 that delve into CoDa. This extensive review aims to furnish crucial insights into the application of control charts for monitoring these distinct data types. Moreover, it offers practical recommendations for practitioners on choosing suitable methods and incorporating machine learning techniques to enhance monitoring efficiency. This guidance is particularly pertinent for monitoring industrial processes within the context of Industry 5.0, reflecting the evolving needs and complexities of modern manufacturing environments.