Background & Purpose: The present study aimed to provide a basis for Iran's success in the next rounds by conducting a data-based evaluation of the factors affecting the success of the Iranian team in the CISM Games using artificial intelligence models. Methodology: Given the exploratory nature of the subject, a qualitative research method was used to determine the most important predictors of the success of countries. First, through preliminary studies, a preliminary list of effective indicators was identified. Then, using the theoretical sampling method, qualitative interviews were conducted with 15 members of the community. Theoretical sampling is a type of purposeful non-probability sampling design that was used in qualitative research. Findings: The results show that 155 indicators can predict the success of countries in the CISM Games. In addition, the analyses performed classify the indicators into 5 components: political, economic, social and cultural, technological and sports (PEST+S). In the next stage, to test the conceptual model, Iran and two countries, Argentina and the United States of America, were randomly selected to compare the predicted 13 indicators with the actual values in 2025, so that on the one hand the possibility of prediction error was reduced and on the other hand, if the predictive indicators were not accurately identified, they could be revised. Finally, according to the results obtained from the artificial neural network (MLP method), the 13 indicators were estimated in 2025 and based on that, the rank of the selected countries in the 2025 CISM Games was estimated. Conclusion: Thus, Iran's rank will be better compared to other countries. According to the findings, sports policymakers of countries can use the identified indicators and components in their future planning for a more successful participation in the CISM Olympic Games.