Predicting the energy consumption of ships is a popular area of study right now, but little is known about the research on predicting the energy consumption of pure electric tugboats. Using vessel operational data and meteorological data, this paper develops six distinct machine learning models for predicting the energy consumption of a pure electric tugboat while it is cruising. It also investigates the impact of meteorological factors on energy consumption prediction. A case study was conducted to predict the power consumption per minute of an all-electric tugboat. According to the experimental data, Random Forest model performs better than other models in terms of prediction accuracy, with a much lower error. After adding meteorological elements as model input variables, the forecast accuracy increases, according to the two sets of comparison studies. This study closes a gap in the literature on predicting the energy consumption of pure electric tugboats and offers marine industry operators and practitioners a high-performing energy consumption prediction model that aids in improving energy utilization efficiency and the creation of driving strategies, tugboat management, scheduling, and charging strategies.