<p>Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean. The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused significant economic losses to the marine industry. Red tide prediction can alleviate and even stop the long-term damages to marine ecosystems, which helps maintain the ecological balance of the ocean environment and contributes to the Sustainable Development Goal of “life below water” formulated by the United Nations. Aiming at red tide prediction using remote sensing technology, this study proposed a novel approach of red tide prediction using time-series hyperspectral observations, and examined the proposed method in the Xinghai Bay, China. Three spectral indices, namely the two-band ratio (TBR), the three-band spectral index (TBSI), and the fluorescence baseline height (FLH), were used to reduce the dimensionality of hyperspectral data and extract spectral features. Two machine learning models including the random forest (RF) and the support vector machine (SVM) were employed to predict whether red tide would occur on a target day based on the time-series spectral indices obtained in the previous days. By comparing and analyzing the prediction results of multiple machine learning models trained with different spectral indices and temporal lengths, it is found that both the RF and the SVM models can predict the red tide outbreaks at the accuracies over 0.9 using adequate temporal lengths of input data. When the temporal length of input data is limited, however, it is suggested to use the RF model, which accurately predicts red tide outbreaks using the temporal input of the 2-d TBSI. The proposed method is expected to provide oceanic and maritime agencies with early warnings on red tide outbreaks and ensure the safety of the coastal environment in large spatial scales using optical remote sensing technology.</p>