Artificial intelligence (AI) approaches have proliferated in stability prediction of construction projects in the past decade. However, the application of AI approaches did not reach the peak of its potential due to the inappropriate handling of missing data and the omission of state-of-the-art techniques. In the present contribution, we proposed a hybrid method for the improved stability prediction of construction projects based on individual machine learning (ML) algorithms, input missing data imputation, semi-supervised learning and the classifier ensemble. Seven ML algorithms were selected to build individual classifiers for the classifier ensemble. 5-fold cross validation was used as the validation method and the performance measures were chosen to be the confusion matrix, the receiver operating characteristic (ROC) curve and the area under ROC curve (AUC). Exhaustive grid search and firefly algorithm were used for hyper-parameters and weights tuning respectively. The capability of the proposed method was verified using an underground construction dataset, the stope hangingwall (HW) dataset. The case study shows that the input missing data imputation and semi-supervised learning improved the predictive performance of ML algorithms on HW stability prediction. The highest and average AUC values on the testing set were increased to 0.954 and 0.923 respectively on the expanded dataset, compared with 0.879 and 0.860 on the original complete dataset. Further improvement was obtained through the classifier ensemble, with the AUC value being increased to 0.976. Harnessing such method extends recent efforts for stability prediction in construction projects, and can significantly accelerate the project design and stability management.