Science, technology, and innovation policy-making laboratories, by providing policymakers with educational, research, and consulting services, before the introduction of policy recommendations to the regulatory and legal structures, make it possible to test the policies on a small scale. Thus, the waste of huge resources allocated to the development of political solutions and additional damages may be prevented. On the other hand, future research as a process gives a more complete understanding of the forces of the long-term future, which should be taken into account in developing policy-making, planning, and decision-making. Therefore, future research of science, technology, and innovation policy-making laboratories to synchronize with today's world and for optimal policies is a necessity addressed in the present study. This study was applied in terms of objective and mixed in terms of method (literature review). In this survey, experts' opinions, interaction analysis, and scenarios have been used. The approach of the present study was future research. For data analysis, the SPSS, MicMac, and ScenarioWizard were used. The statistical population included a group of experts in the field of science, technology, and innovation policy-making. The analysis of data related to different states using ScenarioWizard showed that the probability of occurrence of 9 scenarios was more than other scenarios and the probability of occurrence of other 297 scenarios was very small and weak. The initial review of the scenarios showed the relative dominance of static state over desired and critical states. Out of 9 scenarios, 4 scenarios were strong and more probable. Scenario 1 with an impact score of 221 was the strongest, most desired, and most probable scenario facing science, technology, and innovation policy-making laboratories. Scenario 2 with an impact score of 181 was one of the most desired and probable scenarios facing science, technology, and innovation policy-making laboratories. Scenario 3 had an impact score of 102. Under this scenario, 4 key influencing driving forces were desired and 8 key influencing driving forces were static. Scenario 4 had an impact score of 65. Under this scenario, 8 key influencing driving forces were static and 4 key influencing driving forces were undesired.