The AI-DoS methodology begins by validating finite element simulations against mechanics theories and experimental results, ensuring their accuracy. This validated simulation procedure is then used to efficiently generate the large labeled datasets required for machine learning (ML) training, linking packaging geometries (inputs) to reliability life cycles (outputs). Strategies such as adaptive data sampling and ensemble learning are employed to enhance predictive robustness and accuracy, particularly when data volumes are constrained. This Chapter evaluates several supervised ML algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), Kernel Ridge Regression (KRR), and Gaussian Process Regression (GPR), for Wafer-Level Packaging (WLP) reliability prediction. While fast algorithms like K-Nearest Neighbor (KNN) and Polynomial Regression (PR) are efficient, models such as ANN, GPR, and KRR demonstrate superior accuracy, consistently achieving very low average testing errors (often below 1%). Ensemble learning further improves stability and suppresses extreme deviations. Once the AI model is established and trained, it can predict reliability life within milliseconds, enabling rapid structural optimization and transforming the design and development cycles.