Wireless Sensor Network (WSN) nodes rely on hazardous batteries that need constant replacement. We propose WSNs with solar energy harvesters that scavenge energy from the Sun. The key issue with these harvesters is that solar energy is intermittent. We propose machine learning (ML) algorithms that enable WSN nodes to predict solar irradiance so that the node can intelligently manage its own energy. Our ML models were based on historical weather datasets from California (USA) and Delhi (India) between 2010 to 2020. We performed data pre-processing, feature engineering, identified outliers and grid search to determine the most optimized ML model.