Rapid urbanization has led to severe air pollution in China cities, especially fine particulate matter (PM_2.5) pollution, which threatens human health seriously and becomes one of the main air pollutants. The clarification of the spatiotemporal distribution of PM_2.5 and estimating its health impact are essential for joint prevention and control of PM_2.5 pollution. Machine-learning with the merits of the estimation of PM_2.5 concentration has become a research hotspot to fulfill the deficiency of ground monitoring data. Based on the results of epidemiological studies, health effect models have been widely used in PM_2.5 health impact estimation research. In this study, we utilized the real-time PM_2.5 monitoring data, meteorological data, socio-economic data and Normalized Differential Vegetation Index (NDVI) data from 56 air quality monitoring stations in the Pearl River Delta (PRD) of China during 2014-2018, and constructed the random forest model to estimate the PM_2.5 concentration in the PRD during 2000-2018. Then, the Global Exposure Mortality Model (GEMM) model was adopted to estimate the long-term variations of the PM_2.5-related premature mortality in the PRD during 2000-2018. The main results of this study are as follow: (1) the PM_2.5 concentration in the PRD has maintained at about 35 μg/m³ during 2000-2018, showing a spatial differentiation that declined from northwest to southeast. Precipitation, temperature, wind speed and vapor pressure had a negative effect on PM_2.5 concentration, while GDP and population density had a positive effect on PM_2.5 concentration. (2) The population-weighted average PM_2.5 concentration was lower than the arithmetic average PM_2.5 concentration, indicating that there was no obviously spatial matching relationship between population density and PM_2.5 concentration. For instance, Zhaoqing had a high level of PM_2.5 concentration and a low level of population density, while Shenzhen had a low level of PM_2.5 concentration and a high population density. (3) PM_2.5 pollution in the PRD had a significant impact on ischemic heart disease and stroke, but a weak impact on low respiratory infections during 2000-2018. The number of PM_2.5-related premature mortality has increased gradually and mainly concentrated in the center of the PRD, especially the central district of Guangzhou. This study suggests that the government should not only strengthen air pollution control efforts and improve the medical service level in city, but also pay more attention to the urban population structure and guide population migration orderly, which will facilitate the alleviation of the health impact of PM_2.5 pollution and promote healthy urbanization.