This research explores the use of Long Short-Term Memory (LSTM) networks combined with physics-based constraints to predict lake surface temperatures across buoy locations in the Great Lakes. The model leverages historical meteorological and hydrological inputs to learn temporal dynamics, while physics-informed loss functions ensure consistency with thermodynamic principles. (Souvik, Evan, Mitchell, Aakash, Ruining)