The detection and control of the content of heavy metal elements in the soil are of great significance to the restoration of agriculture and the ecological environment. This study used external cavity confinement combined with traditional laser-induced breakdown spectroscopy (LIBS) to obtain soil spectral data. Then machine learning was used to analyze the content of heavy metal elements Ni and Ba in the soil. During the experiment, the delay time was set to 0.5~5 μs, Ni Ⅱ 221.648 nm and Ba Ⅱ 495.709 nm were selected as the target characteristic spectrum to study, and calculated the influence of delay time on the signal-to-noise ratio (SNR), spectral intensity and enhancement factor under two LIBS conditions. Experimental results show that cavity confinement LIBS (CC-LIBS) can increase the target element’s spectral intensity and SNR. As the acquisition delay time increases, the number of plasmas decreases, and the spectral intensity and SNR gradually decrease, then become stable; when the delay time is set to 1 μs, the SNR of the characteristic spectrum of Ni and Ba elements reaches the best under CC-LIBS conditions, which is determined to be the optimal experimental condition for LIBS at this time. Obtain the spectral data of 9 soil samples containing Ni and Ba through optimal conditions. Since there were 12 248 data points for each set of collected spectral information, the principal component analysis algorithm (PCA) was used to reduce the dimensionality of the spectral data under CC-LIBS conditions. After retaining more than 95% of the original soil information, 9 principal components were selected as the quantitative analysis model’s input variables to improve the model’s calculation speed. The Lasso, AdaBoost and Random Forest models in machine learning were used to model and predict the spectral data after PCA dimensionality reduction to realize the quantitative analysis of soil heavy metal elements Ni and Ba. The experimental results show that the Random Forest model has the best prediction performance in the training and test sets compared with Lasso and AdaBoost models. Under the Random Forest model, the correlation coefficient R² of the Ni element in the test set is 0.937, and the root mean square error (RMSEP) is 3.037; the R² of the Ba element in the test set is 0.886, the RMSEP is 90.515. This paper is based on the research of cavity-confinement LIBS technology combined with machine learning to provide theoretical support and technical guidance for the high-precision detection of heavy metal elements.