The electronic packaging (EP) community has transitioned from the costly, time-consuming, and physically limited Design-on-Experiment (DoE) methodology to the more strategic Design-on-Simulation (DoS) technology, thereby securing trustworthy finite element simulation results. This transition reduces physical experiments, shortens design cycles, and lowers costs. The core simulation tool is the Finite Element Method (FEM), which is used to analyze complex physical phenomena, including thermal-induced strains and stresses arising from the mismatch in the coefficient of thermal expansion among components, which can lead to fatigue failure in solder joints. A major inherent challenge of traditional FEM is the inconsistency and subjectivity of results, which are heavily influenced by the individual researcher’s expertise and the unavoidable dependency on mesh size. DoS technology is a modeling verification procedure or standardized validation step executed before full-scale simulation is performed. This procedure ensures consistent and trustworthy predictions by iteratively tuning and verifying theories, material properties, empirical equations, and solution procedures until simulation results match physical test outcomes or literature data. To optimize computational efficiency, especially for complex 3D models, which are computationally intensive, both 2D and 3D FEM are utilized. Crucially, the Multipoint Constraints (MPC) method is employed in 3D simulations to manage complex models by establishing coupling relationships between nodes of varying mesh densities within a single model, drastically reducing element number and computation time while being suitable for nonlinear analysis. Accurate reliability assessment also requires a precise prediction of the post-reflow shape of solder joints, as their geometry strongly influences fatigue life. The primary prediction methods include the geometric Truncated Sphere Method and the Force-Balanced Analytical Solution, both suited for lightweight packages and circular pads. The Energy-Based Method (e.g., Surface Evolver) is noted for its superior accuracy, ability to account for gravitational effects in larger solder balls, and applicability to pads with arbitrary shapes. Further addressing the mesh-dependency issue in FEM, particularly near stress concentration areas, the Finite Volume-Weighted Method characterizes strain response by averaging state variables over a targeted zone. The resulting stabilized equivalent plastic strain can be used consistently as input for reliability life empirical formulations. For highly complex, multi-scale electronic package architectures, equivalent methods simplify detailed structures into homogenized representations to reduce modeling and computational burden. While the Rule of Mixture (ROM) and Inverse Rule of Mixture (IROM) offer fast estimations of equivalent material properties (like Young’s Modulus and CTE) based on volume fraction, they fail to account for differing structural layouts with the same material proportions, often requiring correction factors. Finally, AI-assisted methodologies enhance DoS, notably through the Equation-Informed Neural Networks (EINNs) combined with Bayesian inference (BI). EINN efficiently extracts and refines coefficients for complex, nonlinear constitutive equations (such as Garofalo, Anand, and Chaboche models) based on measurement data. This ensures accurate material curves for simulations and allows prioritized learning of specific data regions. The combination of validated simulation (including 2D/3D FEM) with AI-assisted data generation can also create the massive datasets needed for training predictive AI models, enabling reliability life cycles to be predicted in milliseconds.