<p>Simultaneous seismic inversion integrates multiple seismic datasets, such as reflection, refraction, or amplitude-versus-offset (AVO) data, into a single inversion process. By jointly estimating impedance and density, this technique enhances the resolution and reliability of subsurface models, particularly in complex geological settings where traditional methods may struggle. This study employs the Artificial Gorilla Troops Optimizer (AGTO), a bio-inspired global optimization algorithm, for simultaneous seismic inversion. AGTO minimizes the misfit between synthetic and observed prestack seismic data, achieving global minima of the objective function. Applied to synthetic and field data, AGTO delivers accurate acoustic and shear impedances as well as density models, outperforming traditional global optimization methods such as genetic algorithms (GA). In noisy environments, AGTO maintains high accuracy, withstanding noise levels up to 30%. For real data applications, AGTO achieves a correlation coefficient of 0.92, compared to 0.77 with GA, and demonstrates approximately nine times faster convergence to global minima. Statistical analyses of real and inverted well data highlight AGTO’s superior performance. After 1000 iterations, AGTO reduces fitness error from 1 to 0.25, compared to 0.78 for GA. This significant improvement in accuracy, robustness, and computational efficiency establishes AGTO as an innovative tool for seismic inversion. Its ability to resolve thin layers and withstand noise underscores its potential for reservoir characterization and lithology identification, making it a valuable advancement in geophysical inversion.</p>