Compacted snow is utilized as a building material in various construction and engineering applications across global high-latitude regions. For the safety assessment of snow and ice structures in cold regions, cohesion and internal friction angle are key shear strength parameters for compacted snow. This study investigates 69 test conditions, considering variations in initial density, sintering time, and sintering temperature. Using direct shear tests, the variation patterns of the shear strength of compacted (machine-made) snow under normal pressures below 100 kPa were analyzed. Results show that under high sintering degree conditions and low normal pressures, the shear stress–displacement curve tends to exhibit strain softening. As initial density increases from 450 to 650 kg·m<sup>-3</sup>;, both cohesion and internal friction angle increase linearly. With sintering time increasing from 0 to 60 days, cohesion first rises and then falls, while the internal friction angle steadily decreases. As sintering temperature decreases from -5 to -25 °C, cohesion decreases, whereas the internal friction angle increases slightly. A Genetic Algorithm-Back Propagation (<em>GA-BP</em>) neural network was employed to develop a predictive model for shear strength parameters, providing benchmark values for cohesion and internal friction angle under various conditions. These benchmarks can be adaptively adjusted when additional influencing factors require consideration. This study provides essential strength parameters for the design and construction of compacted snow structures and offers a framework for accounting for the influence of other factors on these parameters.