Wearable sensing electronic systems (WSES) are becoming a fundamental platform to construct smart and intelligent networks for broad applications. Various physiological data are readily collected by the WSES, including biochemical, biopotential, and biophysical signals from human bodies. However, understanding these sensing data, such as feature extractions, recognitions, and classifications, is largely restrained because of the insufficient capacity when using conventional data processing techniques. Recent advances in sensing performance and systemâlevel operation quality of the WSES are expedited with the assistance of machine learning (ML) algorithms. Here, the stateâofâtheâart of the MLâassisted WSES is summarized with emphasis on how the accurate perceptions on physiological signals under different algorithms paradigm augment the performance of the WSES for diverse applications. Concretely, ML algorithms that are frequently implemented in the WSES studies are first synopsized. Then broad applications of MLâassisted WSES with strengthened functions are discussed in the following sections, including intelligent physiological signals monitoring, disease diagnosis, onâdemand treatments, assistive devices, humanâmachine interface, and multiple sensationsâbased virtual and augmented reality. Finally, challenges confronted for the MLâassisted WSES are addressed.