Scanning Tunneling Microscopy Hydrogen Resist Lithography (STM-HRL) is an advanced fabrication method that enables the creation of electronic devices in silicon with true atomic precision. This technique utilizes the probe of an STM to selectively pattern a single atomic layer of hydrogen on an atomically pristine silicon surface, forming a chemically resistant mask. This mask allows for the precise placement of substitutional dopant atoms, introduced through gas-phase molecular precursors. STM-HRL represents the most precise method for semiconductor device fabrication. While the potential of STM-HRL to drive groundbreaking technological advancements is immense, achieving this vision remains a significant challenge. Presently, device fabrication depends on skilled STM operators, each device being a time-consuming process. By integrating machine learning to assist or even replace human operators in tasks such as real-time image analysis, it may be possible to greatly enhance the complexity, yield, and efficiency of atomically precise device fabrication. In this work, we outline the full STM-HRL fabrication process and examine opportunities for machine learning integration, with a particular focus on using image recognition to identify and map dopant atom distributions and atomic-scale defects, aligning these features within STM-HRL-fabricated device structures.