This study applies the Random Forest (RF) machine learning (ML) algorithm to GNSS radio occultation (GNSS-RO) data for two critical space weather forecasting tasks: the prediction of ionospheric amplitude scintillation (S4 index) and the classification of geomagnetic storm occurrence. For S4 index prediction, an RF regression model was trained on a comprehensive feature set derived from interplanetary magnetic field components, geomagnetic indices, solar radio flux, and historical S4 statistics. The model's architecture is optimized, and Recursive Feature Elimination with Gini Importance (Mean Decrease Impurity) method is applied to identify the most predictive features. For storm detection, an RF classifier was trained on parameters including Total Electron Content (TEC), S4 indices, and key solar and geomagnetic variables to distinguish between storm and non-storm conditions. The optimized S4 prediction model achieved high precision with a Mean Absolute Error of 0.007263 and an R² score of 0.517352. Feature selection via Gini Importance significantly improved model efficiency, increasing the Adjusted R² by 50.5%. The geomagnetic storm classifier demonstrated a critical strength of recall of 0.99 for storm events, ensuring missing only 1% of actual storms. Analysis of feature importance confirmed the model's physical validity, correctly identifying the Kp index, F10.7 solar flux and Dst index as the primary drivers for prediction, which aligns with established solar-terrestrial physics. The results demonstrate the high potential of machine learning, specifically Random Forests, for precise space weather forecasting.