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Cerebral small vessel disease (CSVD) can manifest as brain lesions visible on magnetic resonance imaging, including white matter hyperintensities (WMH), cerebral microbleeds (CMB), perivascular spaces (PVS), lacunes, and recent small subcortical infarcts (RSSI). Detection and segmentation of these imaging markers can provide valuable information on brain health, including prevention and treatment of dementia. However, manual segmentation is cumbersome, especially for large cohorts in research studies. There has been extensive research into the development of automated tools using machine learning to increase accuracy and efficiency in lesion segmentation. This systematic review aimed to summarize novel automated methods developed over the last 10 years that segment CSVD lesion types and have been validated on a population with or at risk for CSVD (e.g., older adults, those with cognitive disorders, or those with vascular risk factors). A search on Web of Science and PubMed yielded 2764 studies, of which 89 were included after screening and full text review. 59 of these methods segmented WMH, 23 detected or classified CMB, 6 detected or segmented PVS, 5 detected, classified, or segmented lacunes, and 2 segmented RSSI. Of these, 30 studies (23 for WMH, 5 for CMB, 1 for PVS, and 1 for lacunes) included links to download code or pre-trained models, including one commercial tool, and one that relied on a commercial tool for input. Overall, this review found good evidence for high quality tools available for WMH segmentation, with fewer tools available to accurately segment other CSVD lesion types.