This presentation examines how machine learning can support scientific discovery in evolving, dynamic systems. Five common ML use cases in experimental science are introduced — dimensionality reduction, structure discovery, behavioral prediction, temporal change detection, and knowledge extraction under limited data — providing a conceptual framework that guides the subsequent applied examples. Three domains illustrate these ideas in practice. In cybersecurity, ML is used for network traffic analysis and anomaly detection, highlighting the critical role of data representation and the often-overlooked statistical pitfalls of real-world deployment. In environmental science, digital twin architectures and time series forecasting methods are applied to the monitoring and prediction of natural resources such as groundwater levels. In medicine, persistent homology is leveraged to uncover hidden structure in the longitudinal microbiome data of cystic fibrosis patients, revealing cyclic dynamics that conventional ML approaches had failed to detect. A recurring theme across all examples is that methodological rigor, domain understanding, and thoughtful data representation are as decisive as algorithm choice when applying ML to real scientific problems.