The increasing demand for energy-efficient and high-quality tire manufacturing requires advanced control strategies capable of handling the nonlinear and time-varying characteristics of the vulcanization process. Conventional control methods often struggle to maintain optimal operating conditions and minimize energy consumption under varying technological and material parameters, which has led to growing interest in intelligent data-driven control approaches. In this study, an adaptive neuro-fuzzy inference system (ANFIS)–based model is developed for intelligent modeling and optimization of the tire vulcanization process in a steam-heated vulcanization press. Four key process variables, vulcanization temperature, steam pressure, tire mass, and process time, are selected as input parameters, while the gas consumption rate is considered as the output control variable. These variables are chosen based on the physical characteristics of the process and practical operating conditions in light-vehicle tire production. A dataset consisting of 250 experimentally consistent process samples is used to train and validate the ANFIS model using the Python programming environment. The model is optimized using Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Gradient Boosting-based tuning strategies. Model performance is evaluated using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). The results demonstrate that the optimized ANFIS model effectively captures the nonlinear relationship between the process parameters and gas consumption. The best-performing configuration achieves a high predictive accuracy with R² ≈ 0.945, while maintaining low prediction errors (RMSE = 0.018–0.025 m³/min, MAE = 0.012–0.017 m³/min). These findings confirm the effectiveness of the proposed approach and its potential for intelligent and energy-efficient control of tire vulcanization processes.