Accurate, consistent, and long-term monitoring of global lake dynamics is essential for understanding the impacts of climate change and human activities on water resources and ecosystems. However, existing methods often require extensive manually collected training data and expert knowledge to delineate accurate water extents of various lake types under different environmental conditions, limiting their applicability in data-poor regions and scenarios requiring rapid mapping responses (e.g., lake outburst floods) and frequent monitoring (e.g., highly dynamic reservoir operations). This study presents a novel remote sensing framework for automated global lake mapping using optical imagery, combining single-date and time-series algorithms to address these challenges. The single-date algorithm leverages a multi-objects superposition approach to automatically generate high-quality training sample, enabling robust machine learning-based lake boundary delineation with minimal manual intervention. This innovative approach overcomes the challenge of obtaining representative training sample across diverse environmental contexts and flexibly adapts to the images to be classified. Building upon this, the time-series algorithm incorporates dynamic mapping area adjustment, robust cloud and snow filtering, and time-series analysis, maximizing available clear imagery (>80 %) and optimizing the temporal frequency and spatial accuracy of the produced lake area time series. The framework’s effectiveness is validated by Landsat imagery using globally representative and locally focused test datasets. The automatically generated training sample achieves commission and omission rates of ∼1 % compared to manually collected sample. The resulting single-date lake mapping demonstrates overall accuracy exceeding 96 % and a Mean Percentage Error of