###### Abstract  
The rapid expansion of international maritime transportation has led to rising greenhouse gas emissions, exacerbating climate change and environmental sustainability concerns. According to the International Maritime Organization, carbon dioxide (CO\({}_{2}\)) emissions from vessels are projected to increase by over 17% by 2050. Traditional emission estimation methods are prone to inaccuracies due to uncertainties in emission factors, and inconsistencies in fuel consumption data. This study proposes deep learning-based CO\({}_{2}\) emission prediction models leveraging engine operation data. Unlike previous approaches that primarily relied on fuel consumption, this model incorporates multiple parameters capturing the relationship between combustion characteristics and emissions to enhance predictive accuracy. We developed and evaluated individual models--convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN)--as well as hybrid model (TCN-LSTM). The hybrid model achieved the highest predictive performance, with a coefficient of determination of 0.9726, outperforming other models across multiple quantitative metrics. These findings demonstrate the potential of deep learning for vessel emission assessment, providing a scientific basis for carbon management strategies and policy development in the international shipping industry. This study thus holds major academic and industrial value, advancing the field of deep learning-based emission prediction and extending its applicability to diverse operational scenarios. v 2025 21 April 2025 21 April 2025 21 April 2025 21 April 2025  
emissions [5]. Projections indicate that emissions from the shipping sector could increase by up to 17% by 2050. In response, the IMO has introduced regulatory measures such as the Energy Efficiency Design Index, Energy Efficiency Existing Ship Index, and Carbon Intensity Indicator as part of its global carbon reduction strategy [6]. To achieve net-zero GHG emissions, annual statistics are compiled by analyzing CO\({}_{2}\) emissions across various industries, including on-road mobile sources such as automobiles [7]. In sectors such as automotive, construction, and manufacturing, extensive research has been conducted to measure and analyze emissions, leading to the development of emission factors with relatively low uncertainty [8; 9; 10]. However, direct measurement of emissions from vessels presents significant challenges due to the high costs and time required for installing monitoring equipment, evaluating emissions under varying environmental conditions, and establishing emission factors. Consequently, many studies have employed modeling techniques that incorporate vessel navigation direction and airflow characteristics to estimate exhaust emissions indirectly. Vessel emissions are typically estimated using two approaches: the top-down and bottom-up methods [11]. The top-down method calculates emissions based on fuel sales or global fuel consumption data [12]. In contrast, the bottom-up method estimates emissions using operational data from individual vessels, offering greater accuracy in reflecting vessel-specific characteristics and operating conditions [5]. Consequently, the bottom-up approach is more widely adopted for regional and vessel-specific emission assessments [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. However, the bottom-up method has several limitations. It does not account for emission variations caused by technical differences in engine performance, and calculation errors may arise due to uncertainties in emission factors and reliance on average values. Additionally, discrepancies between reported and actual fuel consumption can lead to significant deviations in emission estimates [5]. To address these limitations, recent research has explored emission prediction and assessment models leveraging artificial intelligence (AI), including deep learning and machine learning. [PERSON] et al.