###### Abstract
(2023) developed a prediction model combining a convolutional neural network (CNN) and long short-term memory (LSTM) using engine data such as revolutions per minute (RPM), torque, fuel consumption, and nitrogen oxide (NO\({}_{\rm x}\)) emissions from a diesel engine test bench. The model achieved a coefficient of determination (R\({}^{2}\)) of 0.977 and a mean absolute percentage error (MAPE) of 18.4% [15]. [PERSON] et al. (2024) proposed an artificial neural network (ANN) model for predicting NO\({}_{\rm x}\) and carbon monoxide (CO) emissions, utilizing emission measurement data, RPM, shaft power, speed, and wind direction from operating vessels. Their approach, incorporating vessel-related and weather variables, significantly outperformed the traditional bottom-up method in prediction accuracy [16]. [PERSON] et al. (2023) employed automatic identification system (AIS) data--including vessel speed, position, route, engine power, operating time, mode data, vessel type, and gross tonnage--alongside emissions estimated via the bottom-up method. They developed prediction models using ANN, multiple linear regression, and support vector regression, demonstrating that ANN-based models effectively mitigate the limitations of traditional bottom-up approaches [17]. [PERSON] et al. (2023) collected particulate matter emission data by integrating vessel tonnage, size, power, weather conditions, and AIS data with measured exhaust gas plumes. Their ANN-based prediction model, utilizing 17 input variables, achieved higher accuracy than conventional bottom-up methods [18]. Recently, transformer-based models--originally developed for natural language processing (NLP) and computer vision (CV) [19; 20; 21; 22; 23; 24; 25; 26]--have been actively extended to emission prediction tasks as well. For example, [PERSON] et al. (2022) proposed a time series forecasting (TSF) transformer model to predict exhaust gas emissions from commercial trucks. The proposed model outperformed traditional machine learning models such as gradient-boosted regression tree (GBRT), support vector machine (SVM), extreme gradient boosting (XGBoost), as well as deep learning approaches like LSTM [27]. Similarly, [PERSON] et al. (2024) applied a transformer-based model to predict NOx and CO emissions from gas turbines. Compared to LSTM and CNN, their model demonstrated both superior prediction accuracy and faster execution time [28]. In addition, physics-informed neural networks (PINN) have gained attention for their ability to incorporate physical constraints into deep learning models. For instance, [PERSON] et al. (2024) proposed a PINN-based model to predict NOx emissions from coal-fired boilers by embedding a monotonic relationship between NOx emissions and three typical operating parameters. Their result demonstrated superior prediction accuracy and generalization capability compared to conventional machine learning models [29]. However, existing emission prediction models have several limitations. Most studies primarily rely on external environmental variables, such as fuel consumption and wind direction, to estimate CO2 emissions, with relatively little focus on factors directly influencing engine combustion. Notably, few studies have explicitly analyzed the correlations between engine parameters--such as exhaust gas temperature, maximum cylinder pressure, and operating conditions--and emission characteristics for modeling purposes. This gap is largely due to technical and experimental challenges in collecting comprehensive engine operation data. This study addresses the limitations of existing research by proposing a deep learning model for predicting CO2 emissions from vessel engines using engine operation data. The model incorporates 109 engine parameters, including fuel consumption, operating conditions (RPM, torque, power, etc. ), and cylinder-related variables such as exhaust gas temperature, maximum cylinder pressure, and maximum compression pressure, to enhance prediction accuracy.