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Organic materials with room‐temperature phosphorescence (RTP) are in high demand for optoelectronics and bioelectronics. Developing RTP materials highly relies on expert experience and costly excited‐state calculations. It is a challenge to find a tool for effectively screening RTP materials. Herein we first establish ground‐state orbital descriptors (πFMOs) derived from the π‐electron component of the frontier molecular orbitals to characterize the RTP lifetime (τp), achieving a balance in screening efficiency and accuracy. Using the πFMOs, a data‐driven machine learning model gains a high accuracy in classifying long τp, filtering out 836 candidates with long‐lived RTP from a virtual library of 19,295 molecules. With the aid of the excited‐state calculations, 287 compounds are predicted with high RTP efficiency. Impressively, experiments further confirm the reliability of this workflow, opening a novel avenue for designing high‐performance RTP materials for potential applications. [ABSTRACT FROM AUTHOR]