California’s coastlines are in a continual state of erosion. Although coastal erosion is a natural process, accelerated erosion has become an issue on the global scale (Zhang et al. 2004). Changes to rocky coasts seen in California are long-lasting and need to be studied in greater detail, especially when fronted by beaches (Naylor et al. 2010). Predicting change is difficult as it requires understanding of regional factors such as climate, and local factors like development (Bray & Hooke, 1997). In southern California, coastlines are at risk to erosion from sea-level rise as wave action erodes the beach leading to direct erosion of the cliff face (Sterrett & Flick, 1994). This research examines coastal erosion risk in the San Diego region using a machine learning algorithm and Geographic Information Systems. This research serves as an update to a systematic assessment of coastal erosion risk conducted in 1994 based on descriptive erosion risk criteria from Dana Point to the US-Mexico Border; these nine criteria are used to evaluate current risk. Current risk was determined using data from 1994 to 2018; two maps of coastal erosion risk (1994 and 1994-2018) are compared to evaluate spatial change in criteria distribution and temporal change in risk designation by comparing lengths of High, Moderate, and Low risk coastline. Based on modeling results and collected data for this research, risk has changed from 1994. The most important control on risk for the Current Assessment are coastlines with inadequate set-backs.