This study presents BloomSentry, an intelligent monitoring and forecasting system that combines advanced machine learning algorithms with interactive data visualization to predict the likelihood and severity of algal bloom occurrences. At the core of the system is a Gradient Boosting Regression (GBR) model, optimized through Bayesian hyperparameter tuning and enhanced with the Huber loss function for robustness against outliers in ecological data. The model uses key environmental indicators such as ammonia, pH levels, dissolved oxygen, chlorophyll-a, phytoplankton abundance, wind speed, and wind direction. Water quality data were sourced from the Laguna Lake Development Authority (LLDA), while wind data were acquired from Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA).