<p>Halide perovskites (HPs) exhibit favorable characteristics for photovoltaics and LEDs including optimal optoelectronic properties, bandgap tunability, and defect tolerance. However, these devices are currently limited by their instability and the mechanism behind their degradation is not currently fully understood. Atomic force microscopy (AFM), specifically conductive (c-AFM) and Kelvin-probe force microscopy (KPFM), have been widely utilized to investigate the nanoscale electrical behavior of HPs to further the understanding of the factors driving material and device degradation. In this mini-review, we briefly discuss the operating principles of c-AFM and KPFM and highlight experiments in which these techniques have been employed to investigate the effects of grain boundaries, chemical composition, and environmental factors to the electrical stability of HPs. We also provide an overview of recent applications of machine learning (ML) to automate AFM data extraction and analysis and share a perspective on the opportunities for ML methods in AFM measurements of HPs.</p> Graphical abstract <p></p>