In electronic packaging (EP) applications, machine learning provides a data-driven approach for rapid reliability prediction and parameter inference. This technology offers a significant advantage over conventional methods. While accelerated thermal cycling tests (ATCT) take months and finite element (FE) simulations require days or weeks, ML models can deliver reliability predictions in seconds to minutes, substantially accelerating the design cycle. Furthermore, AI-assisted Design-on-Simulation (DoS) technology ensures consistent prediction outcomes, mitigating inconsistencies often attributed to human factors in traditional simulations. The primary application in EP is supervised regression-type machine learning, which focuses on predicting continuous numerical values. These tasks include forecasting the fatigue life (reliability life cycles) for structures like wafer-level packaging and 3D packaging and extracting material parameters for complex constitutive models (e.g., Garofalo, Chaboche, Anand models). The general ML workflow involves several critical steps: Data Preprocessing is necessary to standardize input and output data ranges to improve modeling performance. Cross-validation (often k-fold) is employed to select robust parameters and prevent overfitting. Grid Search systematically evaluates defined ranges to find the optimal configuration of hyperparameters.