Soil moisture is recognized as a key parameter in agriculture, hydrology, meteorology, and other fields. An efficient means for large-scale monitoring is offered by remote sensing, yet traditional extraction methods are hindered by low accuracy and poor adaptability. In response to the above issues, a soil moisture network extraction service based on the GPT4All large model was proposed, through which accuracy and reliability were enhanced by the integration of AI with remote sensing. Landsat 8 images were processed using remote sensing cloud computing platform to obtain soil line parameters, vegetation indices, coverage, vertical drought indices, and other relevant data. The GPT4All model was employed to optimize the mean pixel dryness index. The GPT4All model was innovatively employed to optimize the modified perpendicular drought index, with dynamic adjustment of correction coefficients, contrast, noise suppression, and moisture thresholds based on regional features and historical data. Compliant with Open Geospatial Consortium standards, the system was equipped with web service interfaces and RESTful APIs, enabling seamless integration between web and server for convenient online extraction and analysis. Taking Yongxing Town in Kaifeng City, Henan Province, China, as the study area, the results show that the GPT4Al large model extraction method outperforms traditional methods in metrics such as root mean square error, mean absolute error, and correlation coefficient. The soil moisture extraction service enhanced by integrating the GPT4All large model demonstrates significantly improved automation and adaptability, enabling high-precision monitoring across diverse climates and land cover regions. This provides universally accessible high-precision soil moisture sensing capabilities for users at all levels, with broad application prospects in agricultural irrigation decision-making, drought monitoring, flood warning, and other fields.