Accurate estimation of shallow water bathymetry is a long-standing scientific and practical challenge with profound implications for coastal management, marine navigation, ecological monitoring, disaster mitigation, and sustainable development. Traditional bathymetric survey methods, while highly precise, are constrained by cost, spatial coverage, and operational complexity, particularly in shallow, turbid, or ecologically sensitive waters. Consequently, satellite-derived bathymetry has emerged as a critical alternative, leveraging multispectral remote sensing data to infer water depth based on light–water interactions. Over the past several decades, research in this domain has evolved from simple empirical and semi-empirical models toward sophisticated machine learning and deep learning approaches capable of capturing complex nonlinear relationships between spectral signals and underwater topography.

This study presents a comprehensive, theoretically grounded research article synthesizing empirical remote sensing bathymetry techniques with contemporary machine learning and attention-based intelligence models. Drawing strictly on the provided literature, the article develops an integrated analytical framework that connects early correlation-based fathoming models, multispectral depth inversion techniques, and comparative multi-source remote sensing studies with modern supervised learning algorithms such as random forests, support vector regression, artificial neural networks, convolutional neural networks, and transformer-based attention mechanisms. By doing so, the research situates bathymetry inversion within a broader computational intelligence paradigm, highlighting how methodological advances in machine learning—originally developed in fields such as legal judgment prediction and natural language processing—offer conceptual and practical insights applicable to marine remote sensing.

The methodology section elaborates in depth on the theoretical foundations of satellite-derived bathymetry, including radiative transfer principles, spectral attenuation behavior, and the assumptions underlying empirical and semi-analytical inversion models. It then extends this discussion to machine learning methodologies, explaining how feature representation, nonlinear regression, ensemble learning, kernel-based methods, and hierarchical neural architectures improve depth retrieval accuracy and robustness. Particular emphasis is placed on attention mechanisms and residual learning, not as direct bathymetric tools, but as transferable conceptual models that inspire adaptive weighting of spectral information and context-aware depth estimation.

The results are presented through descriptive analytical synthesis rather than numerical tabulation, demonstrating consistent trends across studies: machine learning-assisted bathymetry generally outperforms traditional empirical approaches in heterogeneous coastal environments, while deep learning models show superior generalization when sufficient training data are available. The discussion critically evaluates limitations related to data dependency, model interpretability, transferability across regions, and environmental variability, while also outlining future research directions that integrate explainable artificial intelligence, multi-source data fusion, and attention-based architectures tailored specifically for marine environments.

Overall, this article contributes a unified theoretical narrative that bridges marine remote sensing and modern artificial intelligence research. By articulating the conceptual continuity between empirical bathymetric inversion and attention-driven learning systems, it provides a foundation for future interdisciplinary research aimed at developing accurate, scalable, and interpretable satellite-derived bathymetry solutions.