This study aimed to measure total factor productivity, identify the determinants of higher productivity, and evaluate policy scenarios for improving productivity in broiler farms in Chaharmahal and Bakhtiari Province, Iran. The required data were collected through a structured questionnaire administered to 42 broiler farms in 2025. First, the Cobb–Douglas and Translog production functions were estimated, and total factor productivity was subsequently calculated. A binary logistic regression model was then employed to identify the factors affecting the probability of achieving higher productivity levels. Finally, the impacts of alternative policy scenarios on productivity improvement were evaluated. The results indicated that the Cobb–Douglas production function provided a better fit than the Translog specification. The average total factor productivity was estimated at 2.79, with the highest productivity observed in the central region of the province (3.45) and the lowest in the western and southern regions (2.18), indicating considerable regional disparities in production efficiency. The logistic regression results showed that the manager’s age had a significant negative effect on the probability of achieving higher productivity, whereas education level, farming experience, participation in training programs, having an academic background relevant to poultry production, access to financial credit, and the use of automated feeding systems all had positive and statistically significant effects. Among the explanatory variables, having a relevant academic background exerted the largest marginal effect on the probability of belonging to the high-productivity group. Furthermore, scenario analysis revealed that the simultaneous implementation of policies aimed at strengthening human capital, expanding specialized training, modernizing production technologies, and improving access to financial resources generated the greatest increase in the likelihood of achieving higher productivity.