Hydrothermal solidification technology can transform waste clay into high-strength building materials with lower energy consumption. Few machine learning methods have been applied to strength prediction of hydrothermally solidified materials, whose mechanical properties differ from those of conventional cement-based materials by virtue of the production method. In this study, six machine learning methods, random forest (RF), gated recurrent unit (GRU), k-nearest neighbors (KNN), back-propagation neuron network (BPNN), extreme gradient boosting (XGBoost) and gaussian process regression (GPR), were used to develop the compressive strength prediction models of hydrothermally solidified clay. These methods can efficiently predict the compressive strength without time-consuming and costly tests and guide the optimization of the mix proportions, thus facilitating the application of hydrothermally solidified clay. For this purpose, 140 experimental data obtained from hydrothermally solidified clay with different Ca(OH)2 content, dry density, moisture content, curing time and curing temperature were used to proposed the prediction models. In the modeling process, these five effective variable parameters were considered as the modeling input parameters. The performance of these models was compared by correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE). The validity of these models was confirmed by the K-fold cross-validation method, and the influence weight of each input parameter on the prediction results was characterized by sensitivity analysis. The results demonstrated that the GPR model provided the best prediction with an R2 value of 0.989. In the sensitivity analysis, curing time had the greatest influence on the predicted results, followed by dry density, Ca(OH)2 content, curing temperature and water content. Comprehensively considering the cost, energy consumption and compressive strength, the applicable mix proportion of hydrothermally solidified clay includes Ca(OH)2 content (not less than 16.64%), moisture content (10%), curing temperature (not less than 87.35 °C), curing time (18 h) and dry density (1.9 g/cm3).