The objective of this study is to develop a comprehensive model of managerial and technical standards for bodybuilding clubs to enhance service quality, increase customer satisfaction, and improve overall club performance. This model aims to address the lack of standardized practices in the management and technical operations of bodybuilding clubs, which can lead to inconsistencies in service delivery and customer dissatisfaction. This study employed a mixed-methods approach, consisting of qualitative and quantitative phases. In the qualitative phase, data were collected through exploratory interviews and systematic literature reviews, resulting in the identification of key themes and components for the standards model. In the quantitative phase, a survey was distributed to managers and trainers of top-rated bodybuilding clubs in Mashhad, Iran. The data were analyzed using descriptive statistics, structural equation modeling (SEM), and the Smart PLS software to validate the proposed model. The findings revealed that standardization significantly enhances service quality and customer satisfaction in bodybuilding clubs. Key factors such as organizational and individual standardization, standard management, and optimization were identified as critical components. The results from the SEM analysis showed significant positive relationships between these factors, indicating that effective management and optimization of standards can lead to improved service quality and higher customer satisfaction. The study also highlighted the importance of technological integration and soft skills development in enhancing managerial and technical capabilities. The study concludes that the implementation of standardized managerial and technical practices in bodybuilding clubs is essential for improving service quality and customer satisfaction. The proposed model provides a comprehensive framework that can guide club managers in adopting standardized practices. Future research should expand the scope of this study to include a broader geographical area and incorporate longitudinal data to further validate the model.