Land surface temperature (LST) is crucial for understanding earth system processes. We expanded the Advanced Baseline Imager Live Imaging of Vegetated Ecosystems (ALIVE) framework to estimate LST in near-real-time for both cloudy and clear sky conditions at a 5-minute resolution. We compared two machine learning models, Long Short-Term Memory (LSTM) networks and Gradient Boosting Regressor (GBR), using top-of-atmosphere (TOA) observations from the Advanced Baseline Imager (ABI) on the GOES-16 satellite against observations from hundreds of measurement locations for a 5-year period. LSTM outperformed, especially at coarser resolutions and under challenging conditions, with a clear sky R² of 0.96 (RMSE 2.31 K) and a cloudy sky R² of 0.83 (RMSE 4.10 K) across CONUS, based on 10-repeat Leave-One-Out Cross-Validation (LOOCV). GBR maintained high accuracy (R² > 0.90) and ran 5.3 times faster, with only a 0.01-0.02 R² drop. Feature importance revealed infrared bands were key in both models, with LSTM adapting dynamically to atmospheric changes, while GBR utilized time information in cloudy conditions. A comparative analysis against the physically based ABILST product showed strong agreement in winter, particularly under clear sky conditions, while also highlighting the challenges of summer LST estimation due to increased thermal variability. This study underscores the strengths and limitations of data-driven models for LST estimation and suggests potential pathways for integrating these approaches to enhance the accuracy and coverage of LST products.