###### Abstract  
Climate change poses significant threats to oceans, leading to ocean acidification, sea level rise, and sea ice loss and so on. At the same time, oceans play a crucial role in climate change mitigation and adaptation, offering solutions such as renewable energy and carbon sequestration. Moreover, the availability of diverse ocean data sources, both remote sensing observations and in situ measurements, provides unprecedented opportunities to monitor these processes. Remote sensing data, with its extensive spatial coverage and accessibility, forms the foundation for accurately capturing changes in ocean conditions and developing data-driven solutions. This review explores the dual relationship between climate change and oceans, focusing on the impacts of climate change on oceans and ocean-based strategies to combat these challenges. From the artificial intelligence perspective, this study systematically analyzes recent advances in applying deep learning techniques to understand changes in ocean physical properties and marine ecosystems, as well as to optimize ocean-based climate solutions. By evaluating existing methodologies and identifying knowledge gaps, this review highlights the pivotal role of deep learning in advancing ocean-related climate research, outlines existing current challenges, and provides insights into potential future directions. climate change; oceans; remote sensing; impacts; solutions; machine learning; deep learning 2025  
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Understanding the impacts of climate change on oceans and implementing ocean-based solutions rely on large-scale, high-quality ocean data, which serve as the foundation for monitoring changes and predicting trends. These data are obtained from various sources such as satellite remote sensing and in situ measurements. Satellite remote sensing provides large-scale, continuous observations of the ocean surface, enabling global monitoring and long-term trend analysis [4; 5]. In contrast, in situ measurements, collected from buoys, vessels, and other instruments, deliver more precise and localized ocean parameters [6]. Extracting meaningful insights from ocean data using advanced analytical models is a crucial step in understanding ocean dynamics and addressing the impacts of climate change. Traditional methods often struggle to process vast and complex datasets, failing to capture intricate relationships within the data. In recent years, artificial intelligence (AI), particularly deep learning (DL) technologies, has emerged as a powerful tool to analyze large-scale datasets, uncover hidden patterns, and produce accurate forecasts [7; 8; 9]. With these advanced techniques, researchers can better understand the impacts of climate change on oceans and develop more effective solutions to mitigate its effects and enhance resilience. While DL has been increasingly applied to the above-mentioned ocean-related research, a comprehensive review that integrates DL architectures, data sources, application domains, and core methodological challenges is still lacking. To fill this gap, this review synthesizes recent advances in applying DL to both the impacts of climate change on the oceans and ocean-based climate solutions, aiming to address the following key questions:  
* Which DL architectures demonstrate high performance for different types of oceanography problems? * Which physical processes in the ocean are poorly modeled by existing DL approaches and why? * How do different types of oceanography data (satellite, in situ, reanalysis) affect the performance of DL models? * How is the problem of different spatiotemporal resolutions in multisensory data (satellite observations) addressed? The remainder of this review is organized as follows. Section 2 introduces the major types of ocean data, including observational data, model data, and reanalysis data.