The precise as well as effective technique is essential for determining the unknown characteristics of a Solid Oxide Fuel Cell (SOFC) to facilitate the robust design of energy systems utilizing SOFC technology. However, SOFC’s mathematical model presents a complex, nonlinear, multivariate structure as well as includes seven unknown parameters, which causes their parameter identification to be a significant challenge. To address this challenge, this paper presents an enhanced version of the Honey Badger Algorithm (HBA), also known as the Modified Honey Badger Algorithm (MHBA), for evaluating the optimal values of the SOFC unknown model parameters. The parameter identification technique is defined as an optimization challenge aimed at minimizing the voltage-based Sum of Squared Errors (SSE). The performance of MHBA is tested using data from a Siemens-based cylindrical SOFC cell with three different datasets corresponding to different temperatures. The outcomes obtained by MHBA are contrasted with HBA and various other Metaheuristics (MH) optimization techniques. The findings reveal that MHBA achieves the lowest SSE values of 3.34E-05, 5.25E-05, and 7.95E-05 at temperatures of 800, 900, and 940 \(^{\circ}\) C, respectively, demonstrating that MHBA is the most suitable algorithm for SOFC parameter identification. Furthermore, a close match between estimated and experimental I–V curves underscores the effectiveness of MHBA in accurately evaluating unknown parameters across different scenarios. Further, statistical metrics evaluated for statistical analysis confirm that MHBA outperforms among other algorithms. The robustness and reliability of MHBA are also validated through convergence curves analysis, showcasing its superiority in identifying unknown SOFC parameters.