We present a machine learning framework aimed at forecasting Starlink (LEO satellite) network performance at fine spatiotemporal resolution. Our approach combines MLab crowdsourced measurements, weather and forecast features, and dynamic satellite density to predict packet loss, jitter, latency, and throughput. We introduce a composite Weather Index and real-time satellite density per location, and train robust ensemble models with anomaly filtering and median aggregation. Our best models achieve good predictive results with less than 17 ms for latency, and 35 Mbps for throughput. Latency is reliably predictable with meteorological and satellite context, while packet loss and jitter remain challenging. Predictions are limited to periods close to the training data and our results establish a reproducible baseline for short-term, weather-aware Starlink network forecasting.