Secondary electron emission (SEE) is a fundamental physical phenomenon in solid-state physics and surface physics, playing critical roles in electron microscopes, energy spectrometers, electron multipliers and many vacuum-based scientific instruments. Nevertheless, in some engineering scenarios where electron avalanche multiplication occurs, SEE brings severe reliability risks for engineering systems such as particle accelerators and high-power microwave devices, where it may trigger multipactor discharge, electron cloud effect and electrostatic discharge, eventually degrading or even destroying functional hardware. Conventional experimental characterization of SEE yield (SEY) suffers from long testing cycles and high costs; moreover, it is difficult to quantitatively decouple and evaluate the contributions from complex surface modifications in practical engineering scenarios. Therefore, reliable numerical simulation tools are highly demanded to predict SEY for diverse materials and surface configurations. In this work, we presents a simulation method for SEY calculation that is adaptable to various application scenarios, specifically designed to model strategies for either suppressing or enhancing SEY on solid surfaces in practical engineering contexts. Based on three fundamental physical processes in electron–matter interactions including elastic scattering, inelastic scattering, and interface transmission, this approach integrates the Moller cross-section model, a single-scattering model, and an interface transmission model to establish the SEE process model. Using the Geant4 particle simulation platform, a Monte Carlo (MC) simulation method for SEE prediction has been developed. The SEY values are simulated for different model parameters, material types, and surface structures, and the results are compared with experimental data. Two key physical parameters, the secondary electron cutoff energy (E_c) and surface barrier energy loss (E_L), are introduced and calibrated to improve low-energy simulation accuracy within the incident electron energy range from 50 eV to 1000 eV. By tuning E_c and E_L, the calculation accuracy of the SEE model has been significantly improved. After normalization, in the low-energy regime (less than 1.5 times the most probable energy), the SEY discrepancy between simulation and experimental results remains within 10%. Furthermore, the simulated SEY values for both compound solids (including Al₂O₃, MgO, SiO₂, TiN) and elemental solids (including Al, Si) show well agreement with public experimental data. SEY calculations are also performed for typical SEY modulation methods including nanoscale surface coating engineering and surface microstructure engineering. The simulated trends closely align with those observed experimentally. Specifically, for coating thicknesses below 10 nm, increasing thickness causes the surface SEY to transition from the substrate level toward that of the thin film material. For the Si substrates coated with Al₂O₃ and MgO, when the thickness increases from 0 to 3 nm, the maximum SEY rises from 1.67 (bare Si) to 4.36 and 5.76, respectively. Periodic cylindrical and square pore microarray structures effectively suppress SEY for both Al₂O₃ and TiN samples; SEY decreases and becomes saturated with increasing aspect ratio and surface porosity. Across all tested microstructure samples, the average deviation of maximum SEY between simulation and experimental measurements remains below 10 %. Merely, limited by idealized geometry setup, the current model does not incorporate secondary micro-roughness introduced by physical etching, which partly accounts for deviations observed at high incident-electron energies. The simulation method achieves acceptable computational efficiency, with the average computing time per energy point below 30 seconds. It supports flexible configuration for bulk materials, nanometer-thick films and complex micro-geometries. This validated Geant4-based SEE MC model provides an effective numerical approach for SEE-related engineering analysis and surface modification design, and showing promising application prospects for SEY prediction including multipactor suppression and secondary electron effect mitigation.