Accurate cropland mapping is essential for understanding agricultural dynamics in Africa and critical for achieving Sustainable Development Goals (SDGs) such as Zero Hunger. Large-scale cropland mapping encounters several challenges, including the varying landscape characteristics of cropland across different regions, extended cultivation periods, and limited availability of reference data. This study develops a 30 m resolution African annual cropland distribution (namely AFCD) dataset spanning the years 2000 to 2022. To extract this large-scale cropland distribution data, we employed random forest (RF) classification and continuous change-detection (CCD) algorithms on the Google Earth Engine platform. Robust training samples were generated, and a locally adaptive model was applied for cropland extraction. The final output consists of annual binary crop/non-crop maps from 2000 to 2022. Independent validation samples from numerous third-party sources confirm that the map's accuracy is 0.86 ± 0.01. A comparison of the cropland area estimates from AFCD with those of the Food and Agriculture Organization (FAO) for Africa yielded an R2 value of 0.86. According to our estimates, Africa's cropland expanded from 1.9435×108 ha in 2000 to 2.1092×108 ha in 2022, marking a net increase of 8.53 %. Prior to 2005, changes in Africa's cropland area were gradual, but after 2006, there has been a marked acceleration in cropland expansion. Despite this continued growth, Africa also experienced significant cropland abandonment. By 2018, abandoned cropland accounted for 11.52 % of the total active cropland area. AFCD also avoided the misclassification of buildings, roads, and trees surrounding cropland common in existing products. The study further highlights the unique advantage of AFCD in providing a dynamic annual cropland dataset at 30 m resolution for Africa. This dataset is a crucial resource for understanding the spatial–temporal dynamics of cropland and can support policies on food security and sustainable land management. The cropland dataset is available at https://doi.org/10.5281/zenodo.14920706 (Lou et al., 2025).