Natural rubber, a crucial industrial raw material worldwide, and a significant part of the income for Laotian farmers, is influenced by alternative planting policies and fluctuations in rubber prices. Aaccurate and rapid monitoring of local artificial rubber plantation is of crucial significance for promoting the sustainable development of the rubber industry. Most research has focused on the extraction of rubber plantation information in small areas, typically utilizing single or dual-temporal image classification, which fails to capture the long-term expansion characteristics of rubber forests. To investigate the long-term, large-scale expansion features of rubber plantation, this study selected a continuous thirty-year Landsat time series imagery from 1990 to 2020. The LandTrendr algorithm was employed to extract changes in rubber plantation across Laos over the three decades. International rubber futures prices and terrain data were also utilized for spatiotemporal change analysis. The selected feature combination, after optimization, can better extract rubber forest distribution. The overall accuracy of rubber forest distribution extraction in 2020 was 89.85%, with a Kappa coefficient of 0.82, revealing Laos’ rubber plantation area to be 321 000 hectares in 2020. The overall accuracy of rubber forest expansion change monitoring using the LandTrendr algorithm and secondary classification was 92.44%, with a Kappa coefficient of 0.82. The expansion of artificial rubber forests in Laos was closely related to the rubber futures market. Through terrain factor analysis, it is deduced that rubber forests are suitable for planting in low-altitude, gently sloping, and sunny areas. This study effectively monitors changes in rubber forests at the national level over an extended period, providing valuable data support for the development of the rubber industry and policy control.