The aerodynamic parameters of a projectile directly affect its flight trajectory, which in turn determines the missile’s design and performance evaluation. Due to the complex aerodynamic environment and the interactions between aerodynamic parameters during highspeed flight, accurately identifying these parameters is a challenging problem. To address this, this paper proposes a combined model using the sparrow search algorithm and kernel extreme learning machine to identify the projectile’s aerodynamic parameters. In order to fully exploit the performance of the SSA and improve identification accuracy, improvements are made to the initialization strategy, convergence factor, and position update strategy of the SSA. The effectiveness of these improvements is validated using the CEC2022 benchmark functions for the improved sparrow search algorithm. Furthermore, the ISSA is employed to optimize the kernel parameters and regularization coefficients of the KELM, leading to the proposed ISSA-KELM identification model. The results show that using the basic extreme learning machine algorithm yields the lowest identification accuracy and fails to capture the nonlinear characteristics of the aerodynamic parameters in certain regions. By introducing a kernel function into the ELM, the KELM method improves identification accuracy by 1 to 4 orders of magnitude. While the KELM and SSA-KELM models still exhibit some discrepancies from the true values in nonlinear regions, the ISSA-KELM model provides the most accurate results, improving accuracy by approximately 4 to 5 orders of magnitude compared to the basic ELM algorithm. This research offers reliable technical support for precise flight trajectory prediction and missile performance optimization.