Accurate information on soil erosion is crucial for sustaining agricultural productivity and managing natural resources. This study aimed to apply empirical models and geo-informatics technology to calculating, classifying and spatial processing erosion severity within the river basin of Chanthaburi Province, Thailand. The methodology is examined soil loss factors and sediment yield using the Universal Soil Loss Equation (USLE) with the Sediment Delivery Ratio (SDR). Rainfall variability and changes in land use and land cover (LULC) were found as the primary factors influencing the terrestrial model. The layer of erodibility (K-factor) was reclassified from soil series data. Additional USLE factors derived from the Digital Elevation Model (DEM) provided by Thailand's Land Development Department (LDD), were integrated. There is a need for more innovative techniques for spatial evaluation to protect the surface runoff. Remote Sensing (RS) and Geographic Information System (GIS) were applied to collect rainfall data and assess the degree of erosion. Statistical analysis revealed a significant difference in annual rainfall between the rain gauge observation and the dataset (p < 0.05). Annual soil erosion in most parts of the study area ranged from very low to moderate severity levels, whereas predictive rainfall data did not indicate any risk intensity. The potential of sediment accumulation increased over the decades, with rates of 128.28, 149.20, 162.58, and 190.92 tons/year for the years 1992, 2002, 2012, and 2022, respectively. Our results compared various rainfall measurements using linear regression and approached some research gaps that provide the development of precipitation data collection for hydrological study. The outcome of these results can serve as scientific data for lithologists to support disaster prevention and guiding decision-making in the anti-erosion planning.