The stars within our Galactic halo presents a snapshot of its ongoing growth and evolution, probing galaxy formation directly. Here, we present our first analysis of the stellar halo from detailed maps of Blue Horizontal Branch (BHB) stars drawn from the SkyMapper Southern Sky Survey. To isolate candidate BHB stars from the overall population, we develop a machine-learning approach through the application of an Artificial Neural Network (ANN), resulting in a relatively pure sample of target stars. From this, we derive the absolute u magnitude for the BHB sample to be ∼ 2 mag, varying slightly with (v − g) 0 and (u − v) 0 colours. We examine the BHB number density distribution from 5272 candidate stars, deriving a double power-law with a break radius of r s = 11.8 ± 0.3 kpc, and inner and outer slopes of α in = −2.5 ± 0.1 and α out = −4.5 ± 0.3 respectively. Through isochrone fitting of simulated BHB stars, we find a colour-age/metallicity correlation, with older/more metal-poor stars being bluer, and establish a parameter to indicate this age (or metallicity) variation. Using this, we construct the three-dimensional population distribution of BHB stars in the halo and identify significant substructure. Finally, in agreement with previous studies, we also identify a systemic age/metallicity shift spanning ∼ 3 kpc to ∼ 20 kpc in galactocentric distance.