For one of the most important aspects for Industry 4.0, cloud-based additive manufacturing (CBAM) deals with many technologies and management-oriented concerns that may inhibit its contribution for development in sustainability. In paper reviews four technical aspects of CBAM are as follows: 3D object database, 3D objects designs, 3D printing processes and cloud service security. For certainty, three managerial concerns are considered based on design engineer, database administrator and process engineer. Then, this study puts forward the idea of optimizing the technical challenges for improving the capacity of applying sustainable 3D printing techniques in smart manufacturing environments, whereas managerial priorities should be attributed for optimizing a 3D printing process. The objective is to accelerate the machine learning (ML) service reliability, a cloud 3D printing service based on smart manufacturing concept ought to consistently offer customer centric 3D objects. To infer the categorical vagueness of the decision makers’ perfective, neutrosophic sets are introduced for the prominence during the decision-making. A popular multi-criteria decision-making (MCDM) method weighted aggregates sum product assessment (WASPAS) is adopted for further data analysis and concluding the most preferred machine learning-based services for a cloud-based additive manufacturing environment. Additionally, sensitivity analysis is performed by the suggested methodology.