Abstract: A major obstacle to the advancements of machine learning models in marine science, particularly in sonar imagery analysis, is the scarcity of AI-ready datasets. While there have been efforts to make AI-ready sonar image dataset publicly available, they suffer from limitations in terms of environment setting and scale. To bridge this gap, we introduce $\texttt{SeafloorAI}$ and $\texttt{SeafloorGenAI}$, the first extensive AI-ready datasets for seafloor mapping across $5$ geographic layers. These datasets, curated in collaboration with marine scientists, facilitate both $\textit{vision}$ and $\textit{vision-language}$-capable machine learning models for sonar imagery. The dataset consists of $62$ geo-distributed data surveys across $17,300$ square kilometers, with $696$K sonar images, $827$K annotated segmentation masks, and approximately $7$M question-answer pairs. By making our data processing source code publicly available, we aim to engage the marine science community to enrich the data pool and inspire the machine learning community to develop more robust models. This collaborative approach will enhance the capabilities and applications of our datasets within both fields.