Polymer optical fibers (POFs) are playing an important role in industrial applications nowadays due to their ease of handling and resilience to bending and environmental effects. A POF can tolerate a bending radius of less than 20 mm, it can work in environments with temperatures ranging from −55 °C to +105 °C, and its lifetime is around 20 years. In this paper, we propose a novel, rigorous, and efficient computational model to estimate the most important parameters that determine the characteristics of light propagation through a step-index polymer optical fiber (SI-POF). The model uses attenuation, diffusion, and mode group delay as functions of the propagation angle to characterize the optical power transmission in the SI-POF. Taking into consideration the mode group delay allows us to generalize the computational model to be applicable to POFs with different index profiles. In particular, we use experimental measurements of spatial distributions and frequency responses to derive accurate parameters for our SI-POF simulation model. The experimental data were measured at different fiber lengths according to the cut-back method. This method consists of taking several measurements such as frequency responses, angular intensity distributions, and optical power measurements over a long length of fiber (>100 m), then cutting back the fiber while maintaining the same launching conditions and repeating the measurements on the shorter lengths of fiber. The model derivation uses an objective function to minimize the differences between the experimental measurements and the simulated results. The use of the matrix exponential method (MEM) to implement the SI-POF model results in a computationally efficient model that is suitable for POF-based system-level studies. The efficiency gain is due to the independence of the calculation time with respect to the fiber length, in contrast to the classic analytical solutions of the time-dependent power flow equation. The robustness of the proposed model is validated by calculating the goodness-of-fit of the model predictions relative to experimental data.