Algal blooms of the dinoflagellate Karenia mikimotoi are seriously threats to the coastal ecosystems, particularly in developing countries. Many previous studies have focused on the effect of nutrients on algal blooms, but the accurate measurement of nutrients can be time-consuming, particularly in remote areas. Currently, the environmental drivers of K. mikimotoi proliferation are far from clearly understood in many coastal zones in the world. As a result of prevailing climate conditions and increasing eutrophication, the Pingtan Special Bay Area in China is prone to frequent red tide events. Based on field work conducted from 2013 to 2019, this study investigated the meteorological and hydrological variables influencing the proliferation of K. mikimotoi. Results showed that cell density of K. mikimotoi was significantly positively correlated with sea surface temperature (SST), air temperature (AT), and dissolved oxygen (DO) concentration, but negatively correlated with sea surface salinity (SSS) and air pressure (AP). A linear model between cell density and SST, SSS, AP, and DO was developed to assess and predict the risk of K. mikimotoi algal blooms (R² = 0.810). Results are of practical significance for preventing and controlling the K. mikimotoi algal bloom in Southeast China. Furthermore, this proposed algal bloom prediction method based on the Linear Regression Model may potentially serve as a valuable reference for algal bloom risk forecasting and management.