Abstract: Mangrove forests are recognized as one of the most biodiverse and productive marine ecosystems globally. This study investigated Beibu Gulf, Guangxi Province. Using Landsat, Sentinel, and PALSAR SAR images from 1985 to 2019 as data sources, as well as the Google Earth Engine (GEE) cloud platform, this study established a multisource dataset by integrating spectral bands, spectral indices, texture features, digital elevation models (DEMs), and backscatter coefficients. Furthermore, 14 classification schemes were developed, and a mangrove remote sensing recognition model was built using an object-based random forest (RF) algorithm. Accordingly, the long-time-series spatiotemporal dynamics of mangrove forests in Beibu Gulf were monitored. The monitoring results show that the object-based RF algorithm demonstrates a high ability to identify mangrove forests. Specifically, Scheme 3 combined with data from 2019 yielded the highest overall accuracy (96.3%) and a kappa coefficient of 0.956, which are 16.3% and 0.195 higher than those of Scheme 1 combined data from 1995, respectively. The classification schemes differed in the producer’s and user’s accuracy of different surface features in the Beibu Gulf. Specifically, these schemes yielded the highest user’s and producer’s accuracy of mangrove forests exceeding 94.6% and 92.0%, respectively. From 1985 to 2019, the area of mangrove forests in Beibu Gulf showed an increasing trend, with an annual changing rate of 6.63%, and the area expanded from inland to coastal areas. The results of this study provide a reference for the protection and sustainable management of mangrove forests while also verifying the feasibility of monitoring long-term spatiotemporal dynamics of mangrove forests based on the GEE platform.