This study explores how machine learning models—Support Vector Machines, Random Forests, and Backpropagation Neural Networks—enhance algal bloom predictions in the Anzhaoxin River Basin. With SVM achieving a 96% accuracy rate, AI-driven early warning systems can help mitigate harmful blooms by analyzing key environmental factors like phosphorus and pH levels. While challenges remain in data collection and regional adaptability, integrating AI with real-time monitoring can revolutionize water management and protect aquatic ecosystems.