The outbreak of cyanobacterial blooms (ABs) caused by water eutrophication has become a global environmental issue, posing severe threats to freshwater ecosystems and human water security. However, existing studies lack uniformity in defining and calculating phenological indicators, making quantitative comparisons challenging. Additionally, limited indicators hinder comprehensive assessments of spatial heterogeneity across large-scale, multi-lake systems. This study utilized daily MODIS imagery from 2000 to 2023 to systematically invert nine phenological parameters across three categories—Frequency, Persistence, and Coverage—for 360 lakes larger than 10 km² in China’s four major lake regions (excluding the Qinghai-Tibet Plateau). Through spatiotemporal analysis, hierarchical clustering, and driving factor analysis, this research aims to clarify the spatiotemporal patterns, classification features, and driving mechanisms of algal bloom phenology in Chinese lakes. Key findings include: （1）63.9% of lakes experienced algal blooms, with 46.5% showing significant increasing trends in ABs. Spatial heterogeneity was evident: southern lakes exhibited earlier outbreaks, central lakes had longer durations, and smaller lakes displayed higher frequency and intensity. All three indicators (Frequency, Persistence, Coverage) generally showed upward trends. (2) Hierarchical clustering based on phenological features categorized lakes into four types: Type 1 (small lakes, short duration but severe outbreaks), Type 2 (large lakes, lowest frequency, duration, and coverage), Type 3 (medium-large lakes, high frequency, early outbreaks, long duration), and Type 4 (medium-small lakes, highest frequency and coverage). Their spatial distribution correlated strongly with regional climate and human activity intensity. (3) Driving factors varied significantly: Frequency and Persistence were primarily influenced by natural factors (temperature, precipitation) (Type 1 and 3 driven by temperature; Type 4 by precipitation), while Coverage was strongly affected by human activities (cultivated land expansion, population density) (Type 1 and 2 driven by population; Type 3 by cultivated land). Only Type 4’s Coverage remained dominated by natural drivers. This study is the first to reveal the spatiotemporal dynamics of algal blooms in Chinese lakes using multi-phenological indicators, clarifying phenological differences and driving mechanisms across lake types. It provides a scientific foundation for regional lake ecological protection and water quality management.