Aiming at the quantitative determination of heavy metal lead in soils, the optimal inversion prediction model of lead content in soils at different pH was studied based on terahertz spectroscopy. Lead-containing soil samples with pH of 8.5, 7.0 and 5.5 were prepared. Terahertz time-domain spectroscopy system TERA K15 was used to collect the Terahertz spectra of the samples, and multivariate scattering correction (MSC), baseline correction and Savoitzky-Golay smoothing were used to pre-process the spectra. For the spectral data of pre-treatment, successive projection algorithm (SPA) was used to select the sensitive frequencies of spectral data. Based on the selected sensitive frequencies, partial least squares (PLS), support vector machine (SVM) and back propagation neural network (BPNN) was used to establish inversion prediction models of lead content in the soil. The correlation coefficient of calibration (Rc), root mean square error of calibration (RMSEC), the correlation coefficient of prediction (Rp), root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were used as model evaluation parameters to evaluate the performance of the model, and to determine the best prediction model of leadship in different pH soils. The experimental results show that the modeling effect after SPA choosing sensitive frequencies is generally better than that of full spectrum. Among them, the best prediction models for the samples with pH 8. 5 were SPA-PLS, Rc, Rp, RMSEC, RMSEP and RPD were 0.997 7, 0.994 6, 14.52 mg·kg-1, 22.70 mg·kg-1 and 9.63, respectively; the best prediction models for the samples with pH 7.0 were SPA-SVM, Rc, Rp, RMSEC, RMSEP and RPD were 0.996 2, 0.975 7, 20.25 mg·kg-1, 33.04 mg·kg-1 and 4.56, respectively; and the samples with pH 5.5 were the best. The prediction models are SPA-BPNN, Rc, Rp, RMSEC, RMSEP and RPD are 0.968 7, 0.974 4, 48.83 mg·kg-1, 55.03 mg·kg-1 and 4.44, respectively. The results provide a new idea for inversion prediction of lead content in different pH soils, and also provide theoretical methods and technical support for other heavy metals inversion prediction models in different pH soils.