Photovoltaic module parameter estimation is a critical step in observing, analyzing, and optimizing the efficiency of solar power systems. To find the best value for unknown parameters, an efficient optimization strategy is required. This paper presents the implementation of the sooty tern optimization (STO) algorithm for parameter assessment of a solar cell/module. The simulation findings were compared to four pre-existing optimization algorithms: sine cosine (SCA) algorithm, gravitational search algorithm (GSA), hybrid particle swarm optimization and gravitational search algorithm (PSOGSA), and whale optimization (WOA). The convergence rate and root mean square error evaluations show that the STO method surpasses the other studied optimization techniques. Additionally, the statistical results show that the STO method is superior in average resilience and accuracy. The superior performance and reliability of the STO method are further validated by the Friedman ranking test.