Organic optoelectronic materials, owing to their exceptional photoelectronic properties, have extensive applications across diverse fields, such as lighting and display, photovoltaic devices, and bioimaging. Machine learning (ML) provides new opportunities for advancing research on organic optoelectronic materials. ML leverages existing datasets to establish robust input-output correlations for predicting material properties, thereby substantially reducing computational costs and enhancing efficiency. This review comprehensively explores recent progress on ML applications for organic optoelectronic material. We focused on three key aspects. First, we review applications ML in predicting photophysical properties of organic dyes, including absorption/emission wavelengths, quantum yields, and aggregation-induced emission/aggregation-caused quenching effects. Second, we examine ML applications in predicting subcellular targeting of fluorescent probes. Third, we discuss the role of ML in screening key descriptors for organic photovoltaics material. The advances in data science position ML as a pivotal tool for elucidating intricate structure-property correlations in molecular systems, driving the accelerated innovation of optoelectronic devices.