A kernel machine is a class of Algorithms that use a (Kernelling) kernel function to implicitly map data into a higher-dimensional feature space, without explicitly computing the mapping. This is useful for handling nonlinear decision boundaries.
The most famous kernel machine is the Support Vector Machine (Support Vector Machines).
Other examples: Kernel Ridge Regression, Kernel PCA.