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Abstract Conjugated polyelectrolytes (CPEs), comprised of conjugated backbones and pendant ionic functionalities, are versatile organic materials with diverse applications. However, the myriad of possible molecular structures of CPEs render traditional, trial-and-error materials discovery strategy impractical. Here, we tackle this problem using a data-centric approach by incorporating machine learning with high-throughput first-principles calculations. We systematically examine how key materials properties depend on individual structural components of CPEs and from which the structure–property relationships are established. By means of machine learning, we uncover structural features crucial to the CPE properties, and these features are then used as descriptors in the machine learning to predict the properties of unknown CPEs. Lastly, we discover promising CPEs as hole transport materials in halide perovskite-based optoelectronic devices and as photocatalysts for water splitting. Our work could accelerate the discovery of CPEs for optoelectronic and photocatalytic applications.