In this paper, a new hybrid AHP and Dempster—Shafer theory of evidence is presented for solving the problem of choosing the best project among a list of available alternatives while uncertain risk factors are taken into account. The aim is to minimize overall risks. For this purpose, a three-phase framework is proposed. In the first phase, quantitative research was conducted to identify the risk factors that can influence a project.Then, a hybrid PCA-agglomerative unsupervised machine learning algorithm is proposed to classify the projects in terms of Properties, Operational and Technological, Financial, and Strategic risk factors. In the third step, a hybrid AHP and Dempster— Shafer theory of evidence is presented to select the best alternative with the lowest level of overall risks. As a result, four groups of risk factors, including Properties,Operational and Technological, Financial, and Strategic risk factors, are considered. Afterward, using an L2ˆ4 Taguchi method, several experiments with various dimensions have been designed which are then solved by the proposed algorithm. The outcomes are then analyzed using the Validating Index, Reduced Risk Indicator, and Solving Time. The findings indicated that, compared to classic AHP, the results of the proposed hybrid method were different in most cases due to uncertainty of risk factors. It was observed that the method could be safely used for selecting project problems in real industries.