Abstract: The mineral flotation process is a complex process with dynamics and uncertainties, which confronts with the problems of accurate soft measurement and optimal control of key indices such as concentration grade and flotation recovery. With the advancement of relevant technologies, important progresses have been made in modeling, control and optimization of mineral flotation process, especially in the data-driven intelligent methods. The research progress of data-based flotation process modeling, control and optimization methods were summarized. First, the descriptions of the flotation progress and corresponding control problem were given in detail. Second, based on operating data and froth images, working condition recognition and index prediction methods were summarized, respectively. Afterwards, intelligent control strategies were introduced from the perspectives of model-based and model-free methods. Then, set-point optimization algorithms with single-objective and multi-objective were reviewed. Finally, future tendencies in the intelligent control of the flotation process were discussed.