This thesis applies both industrial engineering and computational intelligence to demonstrate intelligent systems that solve real-world problems in various smart environments. It presents fundamental concepts and the latest advances in multi-criteria decision-making (MCDM) techniques and their application to smart environments. Though managers and engineers often use multi-criteria analysis in making complex decisions, many core problems are too difficult to model mathematically or have simply not yet been modeled. Firstly, we introduce a real case study in Zagazig, Egypt, for evaluating, operating, and determining the most sustainable location for an EV charging station is presented. Identifying such a location is a complex task that requires considering many influencing factors and their sub-factors. Consequently, a general evaluation system that includes six key factors, in addition to nineteen sub-factors, has been developed. A multi-criteria decision-making (MCDM) approach is applied to deal with this problem. The neutrosophic theory with type-2 neutrosophic numbers (T2NNs) and a type-2 neutrosophic number weighted averaging (T2NNWA) operator are used to combine expert and specialized views since they are ambiguous and uncertain. Firstly, the Decision-making Trial and Evaluation Laboratory (DEMATEL) method is applied to prioritize key factors and sub-factors. Then, the Complex Proportional Assessment (COPRAS) technique selects six sustainable sites for EV charging stations. Also, comparison and sensitivity evaluations verify the approach's reliability, strength, stability, and performance. Secondly, we suggest a novel framework founded on computer-propped diagnosis and IoT to detect and observe type-2 diabetes-infected people. The suggested healthcare system aims to obtain better accuracy of diagnosis with mysterious data. We propose a neutrosophic multi-criteria decision-making (NMCDM) methodology to help patients and physicians to know if the patient is suffering from diabetes or type-2 diabetes, and How to protect the patient from exposure to type-2 diabetes. The suggested methodology is supported by numerical examples on real case studies, to verify the validity of the suggested methodology. Thirdly, we introduce a comprehensive approach through which experts can use linguistic terms and their corresponding spherical fuzzy numbers (SFNs) to express their views on identifying aspects and indicators that affect the sustainability of flue gas treatment systems. Also, determining the optimal system for dealing with emissions is a necessary task, which requires consideration of many aspects of sustainability, including economic, environmental, and technological aspects and their sub-indicators. We propose a new hybrid approach to multi-criteria decision-making (MCDM) under a spherical fuzzy (SF) environment that considers several incompatible indicators. The SF-CRiteria Importance through Intercreteria Correlation (SF-CRITIC) has been used to assess and prioritize the main aspects and sub-indicators. The SF-COmbinative Distance-based ASsessment (SF-CODAS) has been applied to evaluate and rank the selected systems. A sensitivity analysis has been implemented to confirm the effectiveness of the recommended hybrid approach and the stability of its results by changing the weights of the indicators used. Also, a comparative analysis has been fulfilled with MARCOS and WASPAS methods under an SF environment to validate the proposed approach. Furthermore, the conclusions advise optimizing low-emission systems for sintering flue gas in the iron and steel sector to improve sustainability. Fourthly, the autonomous vehicle (AV) is one of the emerging technologies of the new age that has the potential to restructure transportation infrastructure. AVs can sense their surroundings and move around with control and self-sufficiency. AVs can contribute towards reducing traffic congestion on the roads, improving the quality of life, and achieving the highest levels of traffic safety. Thus, this type of vehicle can be integrated into the logistics industry. Due to the presence of several AVs, selecting a standard and efficient AV for logistics planning is a great challenge. The selection of an AV depends on many conflicting and essential criteria. Given its efficiency and reliability in dealing with conflicting criteria, a comprehensive multi-criteria decision-making (MCDM) approach was applied to solve the problem of selecting the optimal AV. However, the MCDM selection process is based on human judgment, which can be ambiguous. Accordingly, uncertainty was handled using type-2 neutrosophic numbers (T2NN). Initially, the method based on the removal effects of criteria (MEREC) was extended under T2NN and employed to assess and prioritize criteria. Then, the combined compromise solution (CoCoSo) method was extended under T2NN and applied to rank the candidate substitutions. To confirm the feasibility of the applied approach, an illustrative case study of four AVs was introduced. A sensitivity analysis was performed by changing the weights of the criteria and some other parameters to confirm the validity and stability of the proposed approach. In addition, a comparison analysis with other MCDM approaches was conducted to show the effectiveness and reliability of the applied approach. Due to the extensive usage of fossil fuels such as coal, oil, and gas, the energy crisis and environmental pollution issues have garnered global attention, making the creation of clean, renewable energy an unavoidable option. Solar photovoltaic energy production is regarded as one of the most promising technologies owing to its safety, dependability, and lack of environmental impact. However, the adoption of photovoltaic systems comes with some risks that may affect their deployment. This part examines the risks of sustainable photovoltaic power plants through a realistic case study. A comprehensive approach is presented through which consultants can use linguistic variables to express their opinions about risks, priorities, and strategies for dealing with them. Evaluating and prioritizing risk assessment is a complex task that requires consideration of multiple criteria. Therefore, we propose a hybrid multi-criteria decision-making (MCDM) approach to deal with risks and their priorities. The risks of five aspects of sustainability were considered: the economic, technical, institutional, social, and environmental aspects. The developed approach consists of two decision-making methods: the Criteria Importance through the Inter-Criteria Correlation (CRITIC) method, which is used to determine the weights of the criteria, and the Evaluation Based on Distance from Average Solution (EDAS) method, which is used to arrange risks according to their priorities. The developed approach is performed in a neutrosophic environment and uses type-2 neutrosophic numbers (T2NN) to address uncertainty. Also, the study adopts a developed model to determine the consultants' reputation weights instead of assuming them. In addition, the model was applied to classify the risks of each aspect of sustainability into three levels, which are high, medium, and normal risks. The results indicate that 52% of the risks in all aspects are high risks, 36% are medium risks, and 12% are usual risks. Also, sensitivity analysis was performed to prove the correctness and stability of the developed approach with differences in weights.