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In the recent five years, data\u2010driven methods using machine learning (ML) algorithms and the knowledge of known materials\/experimental parameters are introduced to OPV studies to help build a quantitative structure\u2010property relationship model and accelerate the molecular design and parameter optimization. Here, these recent promising progresses based on experimental OPV datasets are summarized. This review introduces the general workflow (e.g., dataset collection, feature engineering, ML model generation, and evaluation) of ML\u2010OPV projects and discusses the applications of this framework for predicting OPV performance and experimental optimizations in OPVs. 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