Timely and accurate acquisition of soil moisture information is essential for comprehensively understanding soil water content, improving water use efficiency, and conserving valuable water resources. At present, remote collection of soil moisture data typically requires both soil moisture sensors and wireless transmission modules. However, these approaches are limited to discrete point measurements and cannot achieve comprehensive regional assessment. Moreover, the high cost of soil moisture sensors restricts large-scale deployment. A low-cost method for predicting soil water content over an area was proposed, utilizing the working characteristics of underground wireless signal propagation which was strongly influenced by soil moisture, and its advantages in wireless communication. Based on LoRa technology and a fast machine learning approach with strong generalization capability, a LoRa-based underground wireless sensor network was designed and developed for underground environments to remotely collect data such as received signal strength indicator (RSSI), signal-to-noise ratio (SNR), and soil temperature. Using the collected data, a kernel extreme learning machine (KELM) model optimized by particle swarm optimization (PSO) was proposed to estimate soil water content, addressing the challenges of strong nonlinearity, poor fitting, and low convergence in large-scale data modeling. Experimental results showed that the proposed PSO-optimized KELM model achieved higher prediction accuracy than the extreme learning machine (ELM), support vector regression (SVR), and long short-term memory (LSTM) models. In the training dataset, the KELM model achieved a mean absolute error (MAE) of 0.76% and a root mean square error (RMSE) of 1.02%, while in the testing dataset, the MAE and RMSE were 0.72% and 1.07%, respectively. The proposed method can achieve effective prediction and remote acquisition of soil moisture content without relying on the dense deployment of soil moisture sensors, providing a low-cost solution for large-area soil moisture detection.