During the winters of 2023-2024 and 2024-2025, machine learning (ML) based models were implemented to predict ice thickness at eight sites on the Churchill River and Lake Melville, Labrador for one- and three-day horizons. The forecast ice thicknesses were fed into the Churchill River Flood Forecasting System (CRFFS) operated by the Newfoundland and Labrador (NL) provincial government’s Water Resources Management Division (WRMD). The models were trained on measured ice thickness data from 2017-2023, with the 2024-2025 models additionally trained with data from the 2023-2024 ice season. The 2023-2024 models were deep learning models that used Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs), and the 2024-2025 models were ML models that used a simpler gradient boosting regression (GBR) algorithm. The LSTM (2023-2024) models used a running time-series of local meteorological observations as predictor variables to directly forecast ice thickness, and the GBR (2024-2025) models mainly used forecast surface energy balance variables to predict changes in ice thickness. The average performance of the models across the eight sites was comparable between the two ice seasons; however, the 2024-2025 season models improved performance at key sites on the Churchill River that are critical to ice jam flood forecasting. This paper describes the development of the models and their operation and comparative performance over the 2023-2025 ice seasons.