This study aims to evaluate the treatment effect of agricultural fertilizer non-point source pollution and propose corresponding treatment strategies. The study selected typical agricultural areas in northern China and collected multi-source data, including soil, meteorology, crop growth, and fertilizer application. Through big data and machine learning methods, combined with precision fertilization, green fertilizer promotion, irrigation management, and ecological restoration measures, pollution source analysis, pollution diffusion prediction, and risk assessment were carried out. After the implementation of the treatment measures, the nitrogen and phosphorus content in the soil was significantly reduced, and the concentration of pollutants in water and soil also dropped significantly. Crop yields increased after implementation, verifying the feasibility and effectiveness of the treatment measures. The results show that the combined application of precision fertilization and green fertilizers effectively reduced the pollution risk caused by excessive fertilizer application, and achieved different degrees of treatment effects in different regions. In the future, with the advancement of remote sensing technology, Internet of Things technology, and data analysis algorithms, the treatment of agricultural non-point source pollution will be further improved.