<p>Magnetotellurics (MT) is a geophysical method used to study subsurface structures based on resistivity properties. Inversion modeling in MT is crucial to reconstructing subsurface resistivity distributions from observed data. However, traditional inversion methods often face challenges such as computational complexity, local minimum traps, and dependence on initial models. This study proposes a novel one-dimensional (1-D) MT inversion approach using a One-to-One-Based Optimizer (OOBO) algorithm. OOBO efficiently explores the solution space using information from all population members, reducing the risk of local minima and improving accuracy. The proposed method was tested on synthetic MT data with added noise 5% using a four-layer resistivity model (Model 1) and a five-layer resistivity model (Model 2). The results showed the best resistivity reconstructions with a percentage root mean square error (RMSE) value of 5.98% for Model 1 and 6.40% for Model 2, demonstrating superior accuracy compared to traditional methods such as Occam’s inversion. The algorithm was also applied to field data, demonstrating its ability to efficiently explore model spaces, even when the number of layers is unknown. OOBO’s stochastic nature allows for transparent evaluation of model uncertainty. Comparison with the probabilistic rj-McMCMT approach provided a robust cross-validation benchmark. Despite differing mathematical frameworks, both methods produced highly consistent resistivity structures. This result indicates that OOBO enhances the reliability and efficiency of MT inversion. Furthermore, OOBO demonstrates potential as a competitive approach for evaluating model uncertainty in non-linear electromagnetic problems.</p>