Identification of brain tumor is very important for increasing patient survival but current methods lack some important advantages like the high variability in the shapes of the tumor and low segmentation accuracy. For these reasons, here introduced a new optimization method, the FA and PSO combined approach – referred to as the FFPSO. To overcome these challenges, this proposes the FFPSO: a combination of FA and PSO to improve the optimization in the identification of brain tumors. Combining the inherent exploratory functionality of FA with the exploitative advantages of PSO, FFPSO realizes marked enhancements in detection precision, rate of convergence, and resistance against local optima. The proposed FFPSO is demonstrated to outperform not only standalone FA and PSO but also the other existing hybrid methods in terms of performance on the benchmark medical image datasets. Furthermore, given its versatility, FFPSO's time efficiency in processing medical images makes the tool viable for healthcare transformation. The discussed approach provides several improvements for the general framework of medical image analysis and prospective advancements in early brain tumor diagnosis.