<p>The 2016–2022 monitoring data from three ecological buoys in the Wenzhou coastal region of Zhejiang Province and the dataset European Centre for Medium-Range Weather Forecasts were examined to clarify the elaborate relationship between variations in ecological parameters during spring algal bloom incidents and the associated changes in temperature and wind fields in this study. A long short-term memory recurrent neural network was employed, and a predictive model for spring algal bloom in this region was developed. This model integrated various inputs, including temperature, wind speed, and other pertinent variables, and chlorophyll concentration served as the primary output indicator. The model training used chlorophyll concentration data, which were supplemented by reanalysis and forecast temperature and wind field data. The model demonstrated proficiency in forecasting next- day chlorophyll concentrations and assessing the likelihood of spring algal bloom occurrences using a defined chlorophyll concentration threshold. The historical validation from 2016 to 2019 corroborated the model’s accuracy with an 81.71% probability of correct prediction, which was further proven by its precise prediction of two spring algal bloom incidents in late April 2023 and early May 2023. An interpretable machine learning-based model for spring algal bloom prediction, displaying effective forecasting with limited data, was established through the detailed analysis of the spring algal bloom mechanism and the careful selection of input variables. The insights gained from this study offer valuable contributions to the development of early warning systems for spring algal bloom in the Wenzhou coastal area of Zhejiang Province.</p>