System-in-Package (SiP) technology integrates processors, memory stacks, and radio-frequency modules within millimeter-scale enclosures, generating localized thermal peaks that passive cooling cannot address and that are too costly for finite-element solvers to track in real time. Machine learning offers a tractable alternative, but ensemble and generative families have not been jointly evaluated for this task. This work has three objectives: (1) to assess whether ensemble and generative models can predict node-level temperatures in SiP modules with surrogate-grade accuracy; (2) to quantify how a structural mismatch between a generative training objective and a deterministic regression task affects prediction quality; and (3) to identify the family offering the best trade-off between accuracy, computational efficiency, and interpretability. Three paradigms are compared on a finite-element dataset of 10,201 nodes: Random Forest, Extreme Gradient Boosting, and a Variational Autoencoder using normalized three-dimensional coordinates as inputs. Random Forest delivers the strongest accuracy (mean squared error $0.098$ °C²; coefficient of determination $0.997$); Extreme Gradient Boosting attains the lowest inference latency ($0.0044$ ms per node, $0.8$ MB); the Variational Autoencoder incurs a two-orders-of-magnitude regression penalty consistent with its generative objective but preserves a temperature-coherent latent geometry. Ensemble methods are recommended for accurate, interpretable thermal prediction, while the Variational Autoencoder suits downstream anomaly detection.