This study develops a monthly near-global sea surface wind dataset (MLAWind) from 1950–2023 through an interpretable machine learning framework integrating in-situ observations and satellite data. Evaluations show that MLAWind achieves comparable performance to the widely-used reanalysis datasets at different time scales. It provides reliable historical wind data during both satellite and non-satellite periods, demonstrating broad application prospects in ocean and climate research.