An accurate fuel consumption prediction system for transportation units is crucial for efficient fuel management, offering both cost reduction and emission savings. While extensive research has been conducted on fuel prediction for modes like airplanes, trucks, and vehicles, studies on cargo ships are scarce and often rely on traditional machine learning models. The complexity of real-world factors, such as data collection challenges and varying weather conditions, adds to the difficulty of accurate prediction. This paper addresses these challenges by comparing traditional machine learning algorithms with advanced deep learning models for predicting fuel consumption in ship engines. Our comparative study shows that LSTM-GRU hybrid models emerge as particularly effective, capturing the intricate dependencies and variabilities inherent in fuel consumption forecasting. The results underscore the superior capability of deep learning models, particularly LSTM-GRU, over traditional regression techniques in managing the complexities of fuel consumption in cargo ships.