Process mining (PM) supports organizations by improving their processes using event log data collected from information technology systems. Its primary purposes are discovering actual process models, monitoring and comparing actual and desired workflows, and enhancing processes by considering the discovered model and desired flow. Because process mining gains attraction day by day, various technology companies developed process mining tools to support organizations managing their business processes with data science. Technology selection is a complicated multi-criteria decision-making (MDCM) problem under several criteria and experts’ evaluation, including uncertainty and subjectivity. Spherical fuzzy set is a powerful concept to cope with uncertainty by presenting a wider decision-making area and identifying hesitancy. A fuzzy MDCM approach based on spherical fuzzy AHP is offered in this study to manage the problem of selecting process mining technology under uncertain and ambiguous conditions. Then, one-at-a-time sensitivity analysis is applied to reduce the decision-makers’ subjectivity. This study results in that Price, Process Discovery, Process Analysis&Analytics are the most relevant criteria to decide PM technology. It is interesting that even although Operational Support is one of the less important criteria, it may change the decision on selecting the best PM technology.