Additively manufactured (AM) lattice structures exhibit significant potential in terms of strength-to-mass ratio and spatially graded mechanical properties. In particular, metal lattice structures are increasingly being considered as replacements for bulk components in various industries, including aerospace, space exploration, healthcare, and high-performance automotive applications. However, their widespread adoption remains limited by reliability concerns, primarily due to the high density of fatigue crack nucleation sites and localized stress intensification effects inherent to their architecture. To address these challenges, robust and computationally efficient modeling approaches are essential for predicting fatigue failure and estimating the service life of lattice components. In recent years, our research team has developed an original predictive methodology based on the strain tensor norm. This approach significantly reduces the computational effort required for fatigue life assessment while maintaining accuracy. The method leverages a combined direct and inverse homogenization process, along with the identification of a critical representative volume element (RVE), enabling the application of well-established multi-axial fatigue criteria to cellular materials with reliable results. The proposed fatigue failure prediction framework has been extensively validated through comprehensive experimental campaigns, demonstrating strong agreement between predicted and observed fatigue life across various lattice topologies and loading conditions. Among the tested configurations, a four-point bending setup with a fully reversed load cycle (R = -1) has been specifically implemented using a dedicated load application system, further confirming the robustness of the methodology.