Abstract Accurate representation of raindrop size distribution (DSD) is crucial for understanding precipitation microphysics and improving quantitative precipitation forecasting. Conventional methods in numerical models often prescribe the DSD shape parameter as a constant or derive it from empirical relationships based solely on disdrometer observations, limiting their ability to capture DSD variability driven by dynamic, thermodynamic, and microphysical processes. This study develops a machine learning (ML) framework integrating disdrometer observations with environmental atmospheric variables to retrieve DSD parameters. Trained on data from 11 stations in Shanghai during 2019–2021, three ML models outperform conventional methods, effectively reducing biases and capturing observed DSD variability. They remain robust in independent heavy‐rainfall and tropical cyclone validations during 2022–2025, providing reliable DSD and rain rate estimates. Feature analysis and ablation experiments further underscore the critical role of environmental factors in improving retrieval accuracy, offering a data‐driven pathway to improve DSD parameterizations in numerical models.