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Smart health applications are raising a growing interest around the world thanks to its potential to act proactively and solve health related problems with smart technologies. Smart health technologies can provide effective healthcare services such as personalization of treatments through big data, robotics in cure and care, artificial intelligence support to doctors, etc. The mixed structure of the evaluation of smart health technologies involves various contradictory criteria. However, when information is of uncertain nature, it is difficult to decide on how to treat. A hesitant fuzzy linguistic term set (HFLTS) approach is applied to overcome such uncertainties related to this multi-criteria decision-making (MCDM) problem. This approach can be used to facilitate experts’ decision-making processes in complex and uncertain situations. In this study, an integrated hesitant fuzzy linguistic (HFL) MCDM approach is proposed to evaluate smart health technologies. The criteria are weighted with HFL Analytic Hierarchy Process (AHP), and then, smart health technologies are evaluated with the HFL Combinative Distance-based Assessment (CODAS) method. A comparative analysis with HFL COPRAS and HFL TOPSIS is applied. Lastly, the potential of this approach is presented through a case study.